WO2020258077A1 - Procédé et dispositif de détection de piéton - Google Patents

Procédé et dispositif de détection de piéton Download PDF

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WO2020258077A1
WO2020258077A1 PCT/CN2019/093024 CN2019093024W WO2020258077A1 WO 2020258077 A1 WO2020258077 A1 WO 2020258077A1 CN 2019093024 W CN2019093024 W CN 2019093024W WO 2020258077 A1 WO2020258077 A1 WO 2020258077A1
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image
pedestrian
pedestrian detection
detected
training
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Chinese (zh)
Inventor
李国法
杨一帆
陈耀昱
谢恒�
李盛龙
赖伟鉴
李晓航
朱方平
颜伟荃
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Shenzhen University
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Shenzhen University
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0495—Quantised networks; Sparse networks; Compressed networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning

Definitions

  • This application belongs to the field of computer application technology, and in particular relates to a pedestrian detection method and device.
  • the embodiments of the present application provide a pedestrian detection method and device to solve the problem of inaccurate pedestrian detection results in the prior art.
  • the first aspect of the embodiments of the present application provides a pedestrian detection method, including:
  • the pedestrian detection model Inputting the image to be detected into a pre-trained pedestrian detection model to identify pedestrian data contained in the image to be detected; the pedestrian detection model is trained according to a preset depth separable convolution method;
  • Non-maximum value suppression processing is performed on the pedestrian data, and a portrait frame corresponding to the pedestrian data in the image to be detected is determined.
  • a second aspect of the embodiments of the present application provides a pedestrian detection device, including:
  • the acquiring unit is used to acquire the image to be detected in real time
  • a recognition unit configured to input the image to be detected into a pre-trained pedestrian detection model to identify pedestrian data contained in the image to be detected; the pedestrian detection model is trained according to a preset depth separable convolution method;
  • the determining unit is configured to perform non-maximum value suppression processing on the pedestrian data, and determine a portrait frame corresponding to the pedestrian data in the image to be detected.
  • the third aspect of the embodiments of the present application provides a pedestrian detection device, including a processor, an input device, an output device, and a memory.
  • the processor, input device, output device, and memory are connected to each other, wherein the memory is A computer program that executes the above method in a storage support device, the computer program includes program instructions, and the processor is configured to invoke the program instructions to execute the method of the above first aspect.
  • the fourth aspect of the embodiments of the present application provides a computer-readable storage medium, the computer storage medium stores a computer program, and the computer program includes program instructions that, when executed by a processor, cause the processing
  • the device executes the method of the first aspect described above.
  • the embodiments of the present application have the following beneficial effects: acquiring the image to be detected in real time; inputting the image to be detected into a pre-trained pedestrian detection model, and identifying the pedestrian data contained in the image to be detected; The pedestrian data is subjected to non-maximum value suppression processing to determine the portrait frame corresponding to the pedestrian data in the image to be detected.
  • the pedestrian detection model is obtained by training based on the depth separable convolution method, the acquired image to be detected is recognized according to the pedestrian detection model, and the portrait frame corresponding to the pedestrian contained therein is determined, which not only improves the efficiency of portrait detection, but also This allows the user to determine the corresponding processing method based on the detected portrait frame in the first time, and also improves the accuracy of portrait detection, ensuring that the current pedestrian situation can be clearly detected in the environment with low visibility such as haze.
  • FIG. 1 is a flowchart of a pedestrian detection method provided in Embodiment 1 of the present application;
  • FIG. 2 is a flowchart of a pedestrian detection method provided in Embodiment 2 of the present application.
  • Fig. 3 is an application example of the image enhancement technology provided in the second embodiment of the present application.
  • FIG. 4 is a schematic diagram of the training process and application process of the training model provided in Embodiment 2 of the present application;
  • FIG. 5 is a schematic structural diagram of a pedestrian detection method provided in Embodiment 2 of the present application.
  • FIG. 6 is a schematic diagram of comparison between standard convolution and depth separable convolution provided in the second embodiment of the present application.
  • FIG. 7 is a schematic diagram of the structure of the bottleneck layer in the pedestrian detection method provided in the second embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of a weight connection layer provided in Embodiment 2 of the present application.
  • FIG. 9 is a schematic diagram of the compression incentive mechanism provided in the second embodiment of the present application.
  • FIG. 10 is a distribution diagram of a priori frames of historical images provided in Embodiment 2 of the present application.
  • FIG. 11 is a schematic diagram of the MNPB-YOLO label provided in Embodiment 2 of the present application.
  • FIG. 12 is an example of the detection result provided in Embodiment 2 of the present application.
  • FIG. 13 is a schematic diagram of a pedestrian detection device provided in Embodiment 3 of the present application.
  • FIG. 14 is a schematic diagram of a pedestrian detection device provided in Embodiment 4 of the present application.
  • FIG. 1 is a flowchart of a pedestrian detection method according to Embodiment 1 of the present application.
  • the execution subject of the pedestrian detection method in this embodiment is a device with a pedestrian detection function, including but not limited to devices such as computers, servers, tablets, or terminals.
  • the pedestrian detection method shown in the figure may include the following steps:
  • S101 Acquire images to be detected in real time.
  • the image to be detected in the process of detecting pedestrians, is first acquired in real time.
  • the image to be detected in this embodiment can be in the form of a single image or a piece of video. After the video is obtained, the video can be sampled within a preset period to obtain a single frame of image as the Check the image.
  • the image to be detected in this embodiment may be a color image, a black and white image, an infrared image, etc., which is not limited here.
  • an image or video can be captured in real time through a camera device installed at the front end of the vehicle, such as a driving recorder, to use the image as the image to be detected, and the video is sampled to obtain a single frame The image screen as the image to be detected.
  • a camera device installed at the front end of the vehicle, such as a driving recorder
  • S102 Input the image to be detected into a pre-trained pedestrian detection model, and identify pedestrian data included in the image to be detected; the pedestrian detection model is trained according to a preset depth separable convolution method.
  • a pedestrian detection model is pre-trained, and this embodiment uses deep separable convolution and linear bottleneck layer technology to reduce the amount of calculation and the number of parameters, and improve the operating efficiency of the network.
  • the acquired image to be detected is input into the pedestrian detection model, and the pedestrian data contained in the image to be detected is identified.
  • the pedestrian data in this embodiment may include the position of the pedestrian in the image to be processed, the position of the corresponding pixel, and the pedestrian detection model to find a bunch of boxes corresponding to the pedestrian, etc., which are not limited here.
  • S103 Perform non-maximum suppression processing on the pedestrian data, and determine a portrait frame corresponding to the pedestrian data in the image to be detected.
  • non-maximum suppression processing is performed on the pedestrian data to determine the portrait frame corresponding to the pedestrian data in the image to be detected.
  • non-maximum value suppression is used to suppress elements that are not maximum value.
  • the edge of the corresponding area in the image is determined by means of local maximum search. This part is used to represent a neighborhood, and the neighborhood has two variable parameters, one is the dimension of the neighborhood, and the other is the size of the neighborhood.
  • the feature is extracted through the sliding window, and after classification and recognition by the classifier, each window will get a score.
  • sliding windows will cause many windows to contain or mostly overlap with other windows. At this time, it is necessary to use non-maximum value suppression processing to select those neighborhoods with the highest scores, that is, the probability of pedestrians is the largest, and the probability of not being pedestrians is small, and the windows with low scores are suppressed.
  • the set of bounding boxes corresponding to each pedestrian is B, and the detection frame M with the largest bounding box is selected, which is removed from the set B and added to the final detection result, and each remaining set in B is calculated Intersection over Union (IOU) between the bounding box and the detection frame M, remove the bounding box with IOU greater than or equal to the preset overlap threshold from B, repeat this process until B is empty, and finally The retained bounding box is used as the portrait bounding box corresponding to the pedestrian data in the image to be detected.
  • IOU Intersection over Union
  • the method of suppressing non-maximum values in this embodiment is as follows: first assume that there are a preset number of rectangular boxes, that is, bounding boxes, and sort according to the classification probability of the classifier. Assuming that the number of rectangular boxes is 6, the probabilities of pedestrians from small to large are A, B, C, D, E, and F respectively. Starting from the maximum probability rectangle F, judge whether the IOUs of A to E and F are greater than a set overlap threshold. If the overlap of B, D and F exceeds the overlap threshold, then discard B and D; And mark the first rectangular frame F, which we kept.
  • the image to be detected is acquired in real time; the image to be detected is input into a pre-trained pedestrian detection model to identify the pedestrian data contained in the image to be detected; the pedestrian detection model can be separated according to a preset depth Obtained by training in a product manner; non-maximum suppression processing is performed on the pedestrian data, and a portrait frame corresponding to the pedestrian data in the image to be detected is determined.
  • the pedestrian detection model is obtained by training based on the depth separable convolution method, the acquired image to be detected is recognized according to the pedestrian detection model, and the portrait frame corresponding to the pedestrian contained therein is determined, which not only improves the efficiency of portrait detection, but also This allows the user to determine the corresponding processing method based on the detected portrait frame in the first time, and also improves the accuracy of portrait detection, ensuring that the current pedestrian situation can be clearly detected in the environment with low visibility such as haze.
  • FIG. 2 is a flowchart of a pedestrian detection method according to Embodiment 2 of the present application.
  • the execution subject of the pedestrian detection method in this embodiment is a device with a pedestrian detection function, including but not limited to devices such as computers, servers, tablets, or terminals.
  • the pedestrian detection method shown in the figure may include the following steps:
  • S201 Acquire an image to be detected in real time.
  • the image to be detected in the process of detecting pedestrians, is first acquired in real time.
  • the image to be detected in this embodiment can be in the form of a single image or a piece of video. After the video is obtained, the video can be sampled within a preset period to obtain a single frame of image as the Check the image.
  • the image to be detected in this embodiment may be a color image, a black and white image, an infrared image, etc., which is not limited here.
  • an image or video can be captured in real time through a camera device installed at the front end of the vehicle, such as a driving recorder, to use the image as the image to be detected, and the video is sampled to obtain a single frame The image screen as the image to be detected.
  • a camera device installed at the front end of the vehicle, such as a driving recorder
  • the recognition is performed based on a pre-trained pedestrian detection model. Therefore, before recognizing the acquired image to be detected, a historical image containing a pedestrian is acquired first, so as to train a pedestrian detection model based on the historical image.
  • the form of the historical image in this embodiment is the same as the form of the image to be detected.
  • the historical image in this embodiment may be a color image, a black and white image, an infrared image, etc., which are not limited here.
  • the historical image in this embodiment can be in the form of a single image or a piece of video. After the video is obtained, the video can be sampled within a preset period to obtain a single frame of image as the historical image .
  • S203 Construct a training model according to a preset weight connection layer, and train the training model according to the historical image to obtain the pedestrian detection model.
  • the training model After acquiring the historical image, a training model is constructed according to the preset weight connection layer, and the training model is trained according to the historical image, and a pedestrian detection model is obtained to detect the pedestrian in the image to be detected.
  • the training model in this embodiment includes a first training model and a second training model, where the first training model is used to represent the result obtained after the historical image is expanded Training model, the second training model is used to represent S2031 ⁇ S2034 trained through steps S2031 ⁇ S2034.
  • the two training models can realize training and image recognition separately, or they can be combined for training and image recognition.
  • step S203 may specifically include:
  • a first training model is constructed according to a preset weight connection layer, and the first training model is trained according to the historical image and the corresponding extended image to obtain the pedestrian detection model.
  • Figure 3 shows some application examples of image enhancement technology, where (a) ⁇ (f) are the original image of the historical image, the image after random flip, and the random contrast change.
  • image enhancement technology we added image enhancement technology during training, through random cropping, flipping, color change, affine transformation, Gaussian noise and other operations to expand the original data set to ensure With a strong data set foundation, increase the number of model training and improve the accuracy of model training.
  • the first training model is constructed according to the preset weight connection layer, and the first training model is trained according to the historical image and its corresponding expanded image to obtain a pedestrian detection model.
  • FIG 4 is a schematic diagram of the training process and application process of the training model provided in this embodiment.
  • the historical image in the form of Red Green Blue (RGB) is first obtained, namely RGB image, and then perform image expansion on the RGB image, for example, image cropping, image enhancement, etc., and then through the pre-involved algorithm architecture, namely the MNPB-YOLO pedestrian detection method to recognize the historical image and the expanded image, and Calculate the loss function between the recognition result and the original image according to the preset truth label determination method, and finally use the loss function to update the model parameters in the MNPB-YOLO pedestrian detection method, and finally obtain the fixed parameters and weights in the training model , Get an efficient and accurate training model.
  • RGB Red Green Blue
  • step S203 may specifically include steps S2031 to S2034:
  • S2031 Construct a second training model based on a preset weight connection layer, a preset depth separable convolution method and a preset linear bottleneck layer technology.
  • FIG. 5 is a schematic diagram of the structure of the pedestrian detection method provided by this embodiment, where the multiplication calculation formula between the numbers in FIG. 5 is used to indicate the amount of data currently participating in the calculation.
  • this embodiment proposes a new YOLO-based deep learning method, which includes a basic convolution model part, a weight connection layer, a detection module, and a classification module. Using deep separable convolution and linear bottleneck layer technology to form a basic convolution model, the amount of calculation and the number of parameters are reduced, and the operating efficiency of the network is improved.
  • Figure 6 shows the standard convolution and depth separable convolution provided by this embodiment.
  • H, W are used to represent the height and width of the convolution kernel
  • M is used to represent the number of channels of the input feature map or the number of channels of the convolution kernel
  • N is used to represent the number of convolution kernels.
  • the difference between the effect of deep separable convolution and ordinary convolution is that it can effectively reduce the amount of network parameters and calculations.
  • MNPB-YOLO In order to allow MNPB-YOLO to run on a general processor at a higher speed, we use deep separable convolution Used to build the overall MNPB-YOLO model.
  • FIG. 7 is a schematic diagram of the structure of the bottleneck layer in the pedestrian detection method provided by this embodiment.
  • Depth separable convolution and Relu activation function will cause a certain amount of information loss.
  • a bottleneck layer technology is needed.
  • Table 1 the implementation process of the bottleneck layer technology in this solution is shown in Table 1.
  • the image first undergoes a 3 ⁇ 3 convolution to output a feature map, which is used as the Input in the bottleneck layer structure, as shown in Figure 6.
  • the convolution step size of 1 or 2 different methods are used to perform convolution.
  • the convolution step size is 1, the 1 ⁇ 1 convolution is performed first, and the activation function is Relu6 to increase the dimension, that is, Conv in Figure 7 1 ⁇ 1, Relu6; the multiple of the ascending dimension is reflected in the "expansion coefficient" column in Table 1.
  • the information can be spread more widely in the feature map to prevent depthwise convolution (when the activation function is Relu6) Information loss caused by); use 3 ⁇ 3 Depthwise convolution to reduce the dimension of the feature map after dimensionality, that is, Dwise3 ⁇ 3, Relu6 in Figure 7; then through 1 ⁇ 1 ordinary convolution, the activation function is a linear function To fuse the information of different channels, that is, Conv 1 ⁇ 1, Linear in Figure 7; then the input and this part of the output are added element-wise, that is, ADD in Figure 7, and finally this feature map is output to the next Layer, as the input of the next layer.
  • the difference from convolution stride 1 is that there is no element-wise addition operation. This is because when the convolution stride is 2, the main purpose is It is to down-sample the height and width of the feature map, and the height and width are reduced by one time. The reduced feature map cannot be added element-wise with the original feature map.
  • FIG. 8 is a schematic diagram of the structure of the weight connection layer provided by this embodiment.
  • the weight connection layer can automatically filter the importance of feature maps from different feature scales, and then filter out those unimportant information to improve the performance of the network.
  • the feature maps of different sizes are adjusted to the same size, that is, the space-to-depth transformation, and then all the adjusted feature maps are spliced together to form multi-scale feature information, such as image In the 7x7x256+7x7x192+7x7x320, the important features are finally screened out by the compressed incentive mechanism model.
  • Figure 9 is a schematic diagram of the compression incentive mechanism provided by this embodiment.
  • the compression incentive mechanism is actually a channel attention mechanism. The important ones are selected by assigning different weights ⁇ to each channel. Characteristic, the weight ⁇ is obtained by learning, and the direction of its update is the direction of loss reduction. As shown in the figure, the dimensionality reduction process is performed through 1 ⁇ 1 convolution first, and then the feature value of the channel is obtained through global pooling, and then the weight ⁇ is calculated through the fully connected layer, and then the weight ⁇ is multiplied by the compressed feature map. Finally, the re-calibrated features are obtained.
  • H, W, and C are used to represent the height, width, and number of channels of a feature map, and the superscript'has the same meaning;
  • F tr is used to represent the drop of a 1 ⁇ 1 convolution
  • the purpose of dimensional operation is to reduce the number of channels by way of dimensionality reduction, so as to reduce the amount of calculation required in the following steps, and to improve the efficiency of the method in this embodiment to detect pedestrians;
  • F sq ( ⁇ ) is used to represent a The compression operation is replaced by global average pooling in practice;
  • F ex ( ⁇ ,W) represents the feature information of the shape of 1 ⁇ 1 ⁇ C being mapped to another feature information of the shape of 1 ⁇ 1 ⁇ C, that is, after mapping
  • the feature information of is used to represent the importance coefficient of the channel, and the mapping scheme uses a multi-layer perceptron;
  • W is used to represent the weight of the multi-layer perceptron, and the update direction of this weight is the direction of the loss gradient;
  • S2032 Input the historical image into the second training model, detect pedestrian images in the historical image, and determine the truth value label corresponding to each pedestrian image; the truth value label is used to indicate the recognized history
  • the a priori box of the image is based on the transform coefficient of the truth box.
  • the MNPB-YOLO detection idea is inspired by YOLO. It divides the image into N ⁇ N grids, each grid predicts B detection frames, and each detection frame is composed of the parameters describing the location of the detection frame and the code of the object type.
  • YOLOv1 the first version
  • YOLOv2 the second version
  • which grid the center of the object is located in the detection frame in which grid is used for prediction
  • MNPB-YOLO we first calculate the grid The IOU values of the prior box and the truth box in the grid are then sorted in descending order of the IOU value, and the first k prior boxes are selected to predict the size of the object in a complex manner.
  • FIG. 10 is a distribution diagram of a priori frames of historical images provided in this embodiment.
  • the thick black boxes are two a priori boxes located at the center of the grid.
  • the black grid lines are used to represent the N ⁇ N grid, where N is taken as 7.
  • FIG 11 is a schematic diagram of the MNPB-YOLO label provided in this embodiment.
  • each target person corresponds to a truth label, which is described by 6 parameters, which are used to represent the prior
  • the box represented by the thin line is a priori box
  • the box represented by the thick line is the truth box.
  • w a, h a priori to represent the width and height of the block
  • x a, y a prior frame for indicating the distance from the image of the left edge and upper edge.
  • w g , h g are used to represent the width and height of the truth value box
  • x g , y g are used to represent the distance of the truth value box from the left and upper edges of the image.
  • a, b, c, d are used to indicate that the value here is set at will.
  • the YOLO loss function is as follows:
  • L center , L size, and L score are used to indicate the confidence loss of the center, size, and whether there is an object in the box
  • L class is used to indicate the classification loss
  • c i,j,k Respectively used to represent the confidence and true value of whether there is an object in the detection frame k at the grid I and j of the network prediction
  • x i,j,k ,y i,j,k ,wi ,j,k ,h i,j,k are used to represent the position and size of the detection frame k at the grid I,j predicted by the network
  • the MNPB-YOLO loss is as follows:
  • class i, j, k are used to represent the one-hot encoding of the object type predicted by the network, Used to represent the true value of the object type, The value used to represent the transformation of the prior box of network prediction to the truth box, It is used to represent the true value of the transformation from the prior box to the truth box.
  • ⁇ class and ⁇ box are two constants used to balance different types of losses, and CrossEntropy is used to represent the cross entropy function; It is used to indicate whether the detection box k at grid I, j is used to detect objects; x is used to indicate the input of the function.
  • the features filtered by the weight connection layer need to be further extracted, and finally the specific tensor shape can be output, which is compared with the truth label to constitute a loss.
  • the meaning of the final output shape of the tensor is: divide the image into 7 ⁇ 7 grids, each grid predicts 2 detection frames, and each detection frame requires 6 parameter descriptions, where Including 4 parameters describing the position and size of the detection frame and 2 one-hot codes for distinguishing background and characters.
  • the method parameter amount of this embodiment is much less than that of the original YOLO, which means that the method of this embodiment can run more efficiently.
  • MNPB-YOLO has made major changes in the network structure, using deep separable convolution and bottleneck layer technology to greatly reduce the amount of parameters and calculations, and the proposed weight connection layer can improve network performance , which gives our network the advantages of speed and accuracy at the same time.
  • the network structure of YOLOv1 is inspired by GoogleLenet, the input size is 224 ⁇ 224 ⁇ 3 image, the output grid is divided into 7 ⁇ 7, and each grid predicts 2 detection frames .
  • the detection idea is to use the mapping ability of the convolutional neural network to directly construct the mapping process from the image to the parameters of the detection frame.
  • the detection frame is composed of four parameters: the object center position (x, y) and the object height and width (h, w).
  • the entire network is equivalent to learning how to label the object from scratch.
  • YOLOv2 incorporates this prior knowledge into network prediction. This is what we call a priori box technology.
  • the input size is adjusted to 448 ⁇ 448 ⁇ 3, and the grid is divided into 13 ⁇ 13.
  • Two detection frames are predicted.
  • the size of the prior frame is obtained from the label size in the training set through the clustering method.
  • the network structure has been greatly changed, and the darknet backbone is proposed, and the parameter amount is about half of the original.
  • the comparison of YOLOv1 and YOLOv2 can be seen in Table 3. From Table 3, it can be seen that the method of MNPB-YOLO in this embodiment includes batch standardization, a priori box technology, convolution method, weight connection layer and parameter amount.
  • S204 Input the image to be detected into a pre-trained pedestrian detection model, and identify pedestrian data contained in the image to be detected; the pedestrian detection model is trained according to a preset depth separable convolution method;
  • S204 in this embodiment is exactly the same as that of S102 in the embodiment corresponding to FIG. 1.
  • S205 Perform non-maximum value suppression processing on the pedestrian data, and determine a portrait frame corresponding to the pedestrian data in the image to be detected.
  • Figure 4 is a schematic diagram of the training process and application process of the training model provided in this embodiment.
  • the training can be The model is put into application, that is, pedestrian detection is performed on the image to be detected by training the model.
  • the real-time image that is, the image to be detected
  • the acquired real-time image is input into the trained training model, and at least one frame corresponding to each pedestrian in the real-time image is detected.
  • the large-value suppression method calculates and judges each frame, determines the pedestrian frame corresponding to the pedestrian in the image to be detected, and obtains an accurate detection result.
  • the original YOLO is a multi-target detection model.
  • the modified model is called S-YOLOV1 and S-YOLOV2, in addition, we also compared two traditional methods: pedestrian detection algorithm based on HAAR feature and Adaboost classifier, and pedestrian detection algorithm based on HOG feature and SVM classifier. The test results of different methods are shown in Table 4.
  • AP is used to indicate the average accuracy, specifically the ratio of the area enclosed by the PR curve and the XY axis
  • P is used to indicate the accuracy
  • R is used to indicate the recall rate
  • FPS is used to indicate the processing speed, that is, how many frames are processed per second . It can be seen from Table 4 that the method of this embodiment is not only more accurate than other methods, but also has a much faster speed. The experiment is measured on the Intel-i7 6700K and GTX 1080 computer platforms.
  • FIG. 12 is an example of some detection results in this embodiment. It can be seen from FIG. 12 that the method in this embodiment can detect in the image in the case of haze weather or low environmental visibility. Pedestrians, and can accurately determine the pedestrian frame.
  • S206 Detect the orientation of the pedestrian corresponding to the portrait frame relative to the current vehicle, and the distance between the current vehicle and the pedestrian corresponding to the portrait frame.
  • the pedestrian in the current field of view can be determined according to the portrait frame, and corresponding control operations can be performed.
  • the position of the pedestrian relative to the vehicle and the current distance between the vehicle and the pedestrian can be detected again.
  • the position can be determined according to the area of the pedestrian in the image to be processed. For example, according to the lower right of the portrait frame in the image to be processed, it is determined that the pedestrian is in front of the right of the current vehicle.
  • the distance can be determined by infrared ranging, and the distance between the pedestrian's position and the current vehicle can be determined by an infrared ranging device.
  • S207 Generate reminder information according to the direction and the distance, and broadcast it; the reminder information is used to remind the driver of the current vehicle to pay attention to the pedestrian corresponding to the portrait frame.
  • a reminder message is generated and broadcasted to remind the driver of the current vehicle to pay attention to the pedestrian corresponding to the portrait frame.
  • a vehicle control instruction can be generated according to the position and distance of the pedestrian relative to the current vehicle. , To control the speed and direction of the vehicle through vehicle control commands to avoid crashing into pedestrians.
  • the image to be detected is acquired in real time; the historical image containing the pedestrian is acquired; the training model is constructed according to the preset weight connection layer, and the training model is trained according to the historical image to obtain the pedestrian detection model.
  • Input the image to be detected into a pre-trained pedestrian detection model to identify the pedestrian data contained in the image to be detected; the pedestrian detection model is trained according to a preset depth separable convolution method; for the pedestrian data Performing non-maximum value suppression processing to determine the portrait frame corresponding to the pedestrian data in the image to be detected.
  • FIG. 13 is a schematic diagram of a pedestrian detection device provided in Embodiment 3 of the present application.
  • the pedestrian detection device 1300 may be a mobile terminal such as a smart phone or a tablet computer.
  • Each unit included in the pedestrian detection device 1300 of this embodiment is used to execute the steps in the embodiment corresponding to FIG. 1.
  • the pedestrian detection device 1300 of this embodiment includes:
  • the acquiring unit 1301 is configured to acquire the image to be detected in real time
  • the recognition unit 1302 is configured to input the image to be detected into a pre-trained pedestrian detection model to identify pedestrian data contained in the image to be detected; the pedestrian detection model is trained according to a preset depth separable convolution method ;
  • the determining unit 1303 is configured to perform non-maximum value suppression processing on the pedestrian data, and determine a portrait frame corresponding to the pedestrian data in the image to be detected.
  • the pedestrian detection device further includes:
  • the historical acquisition unit is used to acquire historical images containing pedestrians
  • the training unit is configured to construct a training model according to a preset weight connection layer, and train the training model according to the historical image to obtain the pedestrian detection model.
  • the training unit includes:
  • An expansion unit configured to perform image enhancement processing on the historical image to obtain at least two expanded images corresponding to the historical image
  • the first training unit is configured to construct a first training model according to a preset weight connection layer, and train the first training model according to the historical image and the corresponding extended image to obtain the pedestrian detection model.
  • the training unit includes:
  • the second training unit is used for constructing a second training model based on the preset weight connection layer, the preset depth separable convolution method and the preset linear bottleneck layer technology;
  • the truth value unit is used to input the historical image into the second training model, detect pedestrian images in the historical image, and determine the truth value label corresponding to each pedestrian image; the truth value label is used to indicate The a priori box of the recognized historical image is based on the transform coefficient of the truth box;
  • a loss function unit configured to determine a loss function corresponding to the second training model according to the truth label
  • the optimization unit is configured to optimize the second training model according to the loss function to obtain the pedestrian detection model.
  • the pedestrian detection device further includes:
  • a positioning unit for detecting the orientation of the pedestrian corresponding to the portrait frame relative to the current vehicle, and the distance between the current vehicle and the pedestrian corresponding to the portrait frame;
  • the reminding unit is used to generate and broadcast reminding information according to the orientation and the distance; the reminding information is used to remind the driver of the current vehicle to pay attention to the pedestrian corresponding to the portrait frame.
  • the image to be detected is acquired in real time; the historical image containing the pedestrian is acquired; the training model is constructed according to the preset weight connection layer, and the training model is trained according to the historical image to obtain the pedestrian detection model.
  • Input the image to be detected into a pre-trained pedestrian detection model to identify the pedestrian data contained in the image to be detected; the pedestrian detection model is trained according to a preset depth separable convolution method; for the pedestrian data Performing non-maximum value suppression processing to determine the portrait frame corresponding to the pedestrian data in the image to be detected.
  • FIG. 14 is a schematic diagram of a pedestrian detection device provided in Embodiment 5 of the present application.
  • the pedestrian detection device 1400 in this embodiment as shown in FIG. 14 may include a processor 1401, a memory 1402, and a computer program 1403 that is stored in the memory 1402 and can run on the processor 1401.
  • the processor 1401 executes the computer program 1403, the steps in the foregoing embodiments of the pedestrian detection method are implemented.
  • the memory 1402 is used to store a computer program, and the computer program includes program instructions.
  • the processor 1401 is configured to execute program instructions stored in the memory 1402. Wherein, the processor 1401 is configured to call the program instructions to perform the following operations:
  • the processor 1401 is used to:
  • the pedestrian detection model Inputting the image to be detected into a pre-trained pedestrian detection model to identify pedestrian data contained in the image to be detected; the pedestrian detection model is trained according to a preset depth separable convolution method;
  • Non-maximum value suppression processing is performed on the pedestrian data, and a portrait frame corresponding to the pedestrian data in the image to be detected is determined.
  • processor 1401 is specifically configured to:
  • a training model is constructed according to a preset weight connection layer, and the training model is trained according to the historical images to obtain the pedestrian detection model.
  • processor 1401 is specifically configured to:
  • a first training model is constructed according to a preset weight connection layer, and the first training model is trained according to the historical image and the corresponding extended image to obtain the pedestrian detection model.
  • processor 1401 is specifically configured to:
  • Input the historical image into the second training model detect pedestrian images in the historical image, and determine the truth value label corresponding to each pedestrian image; the truth value label is used to indicate the identity of the recognized historical image
  • the a priori box is based on the transform coefficient of the truth box;
  • the second training model is optimized according to the loss function to obtain the pedestrian detection model.
  • processor 1401 is specifically configured to:
  • a reminder message is generated and broadcast; the reminder message is used to remind the driver of the current vehicle to pay attention to the pedestrian corresponding to the portrait frame.
  • the image to be detected is acquired in real time; the historical image containing the pedestrian is acquired; the training model is constructed according to the preset weight connection layer, and the training model is trained according to the historical image to obtain the pedestrian detection model.
  • Input the image to be detected into a pre-trained pedestrian detection model to identify the pedestrian data contained in the image to be detected; the pedestrian detection model is trained according to a preset depth separable convolution method; for the pedestrian data Performing non-maximum value suppression processing to determine the portrait frame corresponding to the pedestrian data in the image to be detected.
  • the processor 1401 may be a central processing unit (Central Processing Unit, CPU), and the processor may also be other general-purpose processors or digital signal processors (Digital Signal Processors, DSP). , Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
  • the memory 1402 may include a read-only memory and a random access memory, and provides instructions and data to the processor 1401. A part of the memory 1402 may also include a non-volatile random access memory. For example, the memory 1402 may also store device type information.
  • the processor 1401, the memory 1402, and the computer program 1403 described in the embodiments of the present application can execute the implementations described in the first and second embodiments of the pedestrian detection method provided in the embodiments of the present application.
  • the terminal implementation described in the embodiments of the present application can also be implemented, and details are not described herein again.
  • a computer-readable storage medium stores a computer program, the computer program includes program instructions, and the program instructions are implemented when executed by a processor:
  • the pedestrian detection model Inputting the image to be detected into a pre-trained pedestrian detection model to identify pedestrian data contained in the image to be detected; the pedestrian detection model is trained according to a preset depth separable convolution method;
  • Non-maximum value suppression processing is performed on the pedestrian data, and a portrait frame corresponding to the pedestrian data in the image to be detected is determined.
  • a training model is constructed according to a preset weight connection layer, and the training model is trained according to the historical images to obtain the pedestrian detection model.
  • a first training model is constructed according to a preset weight connection layer, and the first training model is trained according to the historical image and the corresponding extended image to obtain the pedestrian detection model.
  • Input the historical image into the second training model detect pedestrian images in the historical image, and determine the truth value label corresponding to each pedestrian image; the truth value label is used to indicate the identity of the recognized historical image
  • the a priori box is based on the transform coefficient of the truth box;
  • the second training model is optimized according to the loss function to obtain the pedestrian detection model.
  • a reminder message is generated and broadcast; the reminder message is used to remind the driver of the current vehicle to pay attention to the pedestrian corresponding to the portrait frame.
  • the image to be detected is acquired in real time; the historical image containing the pedestrian is acquired; the training model is constructed according to the preset weight connection layer, and the training model is trained according to the historical image to obtain the pedestrian detection model.
  • Input the image to be detected into a pre-trained pedestrian detection model to identify the pedestrian data contained in the image to be detected; the pedestrian detection model is trained according to a preset depth separable convolution method; for the pedestrian data Performing non-maximum value suppression processing to determine the portrait frame corresponding to the pedestrian data in the image to be detected.
  • the computer-readable storage medium may be the internal storage unit of the terminal described in any of the foregoing embodiments, such as the hard disk or memory of the terminal.
  • the computer-readable storage medium may also be an external storage device of the terminal, for example, a plug-in hard disk equipped on the terminal, a smart memory card (Smart Media Card, SMC), or a Secure Digital (SD) card , Flash Card, etc.
  • the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.
  • the computer-readable storage medium is used to store the computer program and other programs and data required by the terminal.
  • the computer-readable storage medium can also be used to temporarily store data that has been output or will be output.
  • the disclosed terminal and method may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components can be combined or It can be integrated into another system, or some features can be ignored or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may also be electrical, mechanical or other forms of connection.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments of the present application.
  • the functional units in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.
  • the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium.
  • the technical solution of this application is essentially or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium It includes several instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program code .

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Abstract

La présente invention se rapporte au domaine technique des applications informatiques, et concerne un procédé et un dispositif de détection de piéton. Le procédé consiste : à obtenir une image à détecter en temps réel ; à entrer ladite image dans un modèle de détection de piéton obtenu par pré-entraînement, et à identifier des données de piéton comprises dans ladite image ; et à effectuer un traitement de suppression non maximale sur les données de piéton et à déterminer une trame de portrait correspondant aux données de piéton dans ladite image. Dans ce mode de réalisation, le modèle de détection de piéton est obtenu au moyen d'un entraînement sur la base d'un mode de convolution séparable en profondeur, l'image obtenue à détecter est identifiée selon le modèle de détection de piéton, et la trame de portrait correspondant au piéton comprise dans ladite image est déterminée, ce qui permet non seulement d'améliorer l'efficacité de détection de portrait, de sorte qu'un utilisateur puisse déterminer un mode de traitement correspondant selon la trame de portrait détectée au premier instant, mais également d'améliorer la précision de détection de portrait, garantissant ainsi que la situation actuelle du piéton puisse être clairement détectée même dans l'environnement à faible visibilité telle que la brume.
PCT/CN2019/093024 2019-06-26 2019-06-26 Procédé et dispositif de détection de piéton Ceased WO2020258077A1 (fr)

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