TW201812718A - Method and equipment for testing vehicles - Google Patents

Abstract

A method and equipment for testing vehicles are provided. The method includes: retrieving basic character data from an image of a vehicle; inputting the basic character data into a pre-trained and-or model, obtaining nodes of each layer of the and-or model as key nodes; correlating the key nodes as optimized calculating branches; transforming the key nodes to location parameters of the image of the vehicle, and determining a graph model of each layer's key nodes of the calculating branches according to a preset relationship between each layer's key nodes and the graph models; and determining location information and layout of the vehicle according to the location parameters and the graph model of each layer's key nodes of the calculating branches.

Description

 Vehicle detection method and device  

The invention relates to the technical field of image processing, and in particular to a method and a device for detecting a vehicle.

At present, the identification of vehicle signals is generally realized by an automated supervisory system detecting target objects in a vehicle signal picture, such as detecting a license plate in a vehicle signal picture.

However, due to the diversity of realistic vehicle scenes and the uncontrollable or uncontrollable proportion of the occlusion relationship between vehicles, the identification of current vehicle signals often encounters more interference and the recognition effect is not good.

In addition, the identification of traditional vehicle signals is often carried out using a simple manual setting of features, and the efficiency of the identification work is low when dealing with some complex scenes.

It is an object of the present invention to provide a method and apparatus for vehicle detection.

The method for detecting a vehicle of the present invention includes the following steps: S1, after receiving the to-be-detected picture including the vehicle signal, extracting a basic characteristic signal of the to-be-detected picture by using a predetermined algorithm; S2, inputting the basic characteristic signal to In the And-Or model generated by the pre-training, the hierarchical nodes are obtained by using the And-Or model generated by the pre-training, and the acquired hierarchical nodes are output as key nodes; S3, the key nodes of the output are associated, Taking the associated key nodes of the hierarchy as a preferred calculation branch; S4, converting the key nodes of the hierarchy in the calculation branch into position parameters in the image to be detected, and according to the predetermined key nodes of the hierarchy The association relationship of the graphic template determines the graphic template corresponding to the key nodes of each level in the calculation branch; S5, acquiring the image to be detected according to the position parameter corresponding to the key nodes of the hierarchical level in the calculation branch and the graphic template The vehicle position signal and the vehicle layout relationship are output and output.

In a preferred embodiment, the hierarchy includes at least three of the following: a level of vehicle communication area, a level of distribution location area of each vehicle, and a level of each component component area within the vehicle.

In a preferred embodiment, the step S2 includes: S21, inputting the basic feature signal into the pre-trained And-Or model, and acquiring a vehicle global area, the vehicle global area being represented by the Or node and serving as the And a root node of the -Or model; S22, at the level of the connected area of the vehicle, each vehicle connected area is decomposed based on the root node, the respective vehicle connected areas are respectively represented by different And nodes; S23, the distribution of each of the vehicles At a location area level, an area corresponding to each vehicle is extracted from the respective vehicle communication areas, and an area corresponding to each vehicle is represented by an Or node; and S24, each local part in the vehicle forms an area level, for each Each local component area of the vehicle is represented by the And node and organized; and in S25, each Or node and each And node are output as key nodes.

In a preferred embodiment, before the step S2, the method further includes: S01, acquiring a preset number of vehicle images with vehicle signals as training sample pictures of the And-Or model; S02, extracting a preset ratio The training sample picture is used as a training set, and the remaining training sample picture is used as a test set, and each training sample picture frame in the training set defines a vehicle connection area, a distribution location area of each vehicle, and each interior of the vehicle. The local component constitutes an area; S03, training the And-Or model by using the training sample picture after the frame processing, to train and generate the And-Or model for performing image detection; and S04, taking each training sample picture in the test set The test is input into the And-Or model generated by the training, and if the accuracy of the test is greater than or equal to the preset threshold, the training ends.

In a preferred embodiment, after the step S04, the method further includes: if the accuracy of the test is less than the preset threshold, prompting to increase the number of the training sample pictures.

The device for detecting a vehicle of the present invention includes: an extraction module, configured to extract a basic feature signal of the image to be detected by a predetermined algorithm after receiving the image to be detected including the vehicle signal; The basic feature signal is input into the pre-trained And-Or model to acquire each hierarchical node through the pre-trained And-Or model, and the obtained hierarchical nodes are output as key nodes; a group, configured to associate the key nodes of the output to use the associated hierarchical key nodes as a preferred calculation branch; a transformation module, configured to convert the hierarchical key nodes in the calculation branch into the a location parameter in the picture to be detected, and determining, according to the predetermined association relationship between the key nodes of the hierarchy and the graphic template, the graphic template corresponding to the key nodes of each level in the calculation branch; an output module, according to the The position parameter corresponding to the key nodes of each level in the calculation branch and the graphic template obtain the vehicle position signal in the image to be detected and Vehicle distribution relationships and output.

In a preferred embodiment, the hierarchy includes at least three of the following: a level of vehicle communication area, a level of distribution location area of each vehicle, and a level of each component component area within the vehicle.

In a preferred embodiment, the training module includes: an obtaining unit, configured to input the basic feature signal into the pre-trained And-Or model, and obtain a vehicle global area, where the vehicle global area is Or The node represents and serves as a root node of the And-Or model; a decomposition unit is configured to, at the level of the vehicle communication area, decompose each vehicle communication area based on the root node, and the respective vehicle communication areas are respectively represented by different And nodes; An extraction unit is configured to extract an area corresponding to each vehicle from each of the vehicle communication areas at a level of the distribution location area of each of the vehicles, wherein the area corresponding to each vehicle is represented by an Or node; a unit for forming a regional level in each part of the vehicle, and each local component area of each vehicle is represented and organized by an And node; an output unit is used for the Or nodes and the respective The And node is output as a key node.

In a preferred embodiment, the device for detecting a vehicle further includes: an acquisition module, configured to acquire a preset number of vehicle images with vehicle signals as training sample images of the And-Or model; The training sample picture is extracted as a training set, and the remaining training sample picture is used as a test set, and the vehicle connected area and the distribution of each vehicle are determined for each training sample picture frame in the training set. a location area and each partial component component area of the vehicle; a generating module, configured to train the And-Or model by using the training sample picture after the frame processing, to train and generate an And-Or model for performing image detection; A test module is configured to input each training sample image in the test set into the training-generated And-Or model for testing. If the accuracy of the test is greater than or equal to a preset threshold, the training ends.

In a preferred embodiment, the device for detecting a vehicle further includes: an adding module, if the accuracy of the test is less than a preset threshold, prompting to increase the number of the training sample picture, triggering the frame module to continue Training generates an And-Or model for image detection.

The invention has the beneficial effects that the present invention firstly performs preliminary processing on the to-be-detected picture including the vehicle signal to obtain a basic feature signal, and then inputs it into the pre-trained And-Or model to obtain key nodes of each level, and each level is After the key nodes are associated, as a better calculation branch, for each calculation branch, after obtaining the graphic templates of the key nodes of each level and transforming the position parameters of the key nodes of each level, the position parameters corresponding to the key nodes of each level can be selected. And the graphic template obtains the vehicle position signal and the vehicle layout relationship. In this embodiment, the And-Or model is used to detect and identify the vehicle, and the picture with complex scene can be processed, and the vehicle signal in the picture is effectively recognized and recognized efficiently.

S1~S5‧‧‧Steps

S21~S25‧‧‧Steps

S01~S04‧‧‧Steps

101‧‧‧ extraction module

102‧‧‧ training module

103‧‧‧Association module

104‧‧‧Transformation module

105‧‧‧Output module

201‧‧‧Getting module

202‧‧‧framed module

203‧‧‧Generation module

204‧‧‧Test module

1 is a schematic flow chart of a first embodiment of a method for detecting a vehicle according to the present invention; FIG. 2 is a schematic flowchart of a step S2 shown in FIG. 1; and FIG. 3 is a second embodiment of a method for detecting a vehicle according to the present invention. 4 is a schematic structural view of a first embodiment of a device for detecting a vehicle according to the present invention; and FIG. 5 is a schematic structural view of a second embodiment of a device for detecting a vehicle according to the present invention.

The principles and features of the present invention are described below in conjunction with the accompanying drawings, which are intended to illustrate the invention and not to limit the scope of the invention.

Please refer to FIG. 1 , which is a schematic flowchart of an embodiment of a method for detecting a vehicle according to the present invention. The method for detecting the vehicle includes the following steps.

Step S1: After receiving the to-be-detected picture including the vehicle signal, extract the basic feature signal of the to-be-detected picture by using a predetermined algorithm.

The method of vehicle detection of the present embodiment can be applied to areas such as traffic safety monitoring, automobile production, and automobile insurance in a complicated scene, and a vehicle detecting device having a picture capturing function captures pictures in these scenes, and when the vehicle is captured After the picture of the signal, the picture is taken as the picture to be detected, and the basic feature signals are extracted by some predetermined algorithms.

In this embodiment, the predetermined algorithm is some basic algorithms of image processing, such as an image edge detection algorithm, and the basic feature signal is a picture signal that can be directly input to the And-Or model, such as an image. The location or relationship of each part. In a preferred embodiment, the Histogram of Oriented Gradient (HOG) algorithm is used to obtain the gradient edge signal of the image to be detected, and then the K-means clustering algorithm is used to obtain each The clustering center of the image after the gradient edge or the DPM (Deformable Parts Model) algorithm is used to obtain the mutual positional relationship of each part of the image after the gradient edge.

In step S2, the basic feature signal is input into the pre-trained And-Or model to acquire each hierarchical node through the pre-trained And-Or model, and the obtained hierarchical nodes are output as key nodes.

In this embodiment, the And-Or model is obtained by training a plurality of pictures containing the vehicle signal in advance, and inputting the extracted basic feature signal into the pre-trained And-Or model. The pre-trained And-Or model learns the input basic feature signal. In the learning process, the root node is first obtained, and then the nodes corresponding to the respective levels are obtained based on the root node, and then the nodes corresponding to the respective levels are obtained. Output as a key node.

In the And-Or model generated by the pre-training in this embodiment, the preferred level includes at least three, that is, the level of the vehicle communication area, the distribution level of each vehicle, and the regional level of each part of the vehicle. . Of course, the level can also be less than three or more than three.

In step S3, the outputted key nodes are associated to use the associated key nodes of the hierarchy as a preferred calculation branch.

In this embodiment, after the key node is output, the output key node is associated. The key nodes may be associated with each other according to the root node. Specifically, the key nodes in each level may be associated first, for example, the key nodes in the same level are associated with each other according to the location relationship. Determining the relative positions of the key nodes in the same level; then, associating the key nodes of the respective levels according to the positional relationship, for example, associating the positions of the key nodes in different levels to determine the key nodes in the different levels Relative position, after the key nodes are associated, the architecture of each part of the image to be detected can be outlined, and then the associated key nodes of each level are used as a better calculation in the learning process of the pre-trained And-Or model. Branch for the next step.

Step S4, converting the key nodes in the calculation branch into position parameters in the to-be-detected picture, and determining the key levels in the calculation branch according to the predetermined relationship between the key nodes of the hierarchy and the graphic template. The graphic template corresponding to the node.

In this embodiment, the key nodes in the preferred calculation branch are converted into position parameters in the to-be-detected picture to obtain a specific location of each part in the to-be-detected picture.

In addition, for each level in each of the better calculation branches, the graphic template corresponding to each level key node may be determined according to a predetermined association relationship between the hierarchical key nodes and the graphic template, for example, a key of a certain level If the node is elliptical, the associated graphic template is an ellipse. The graphic template is a line or a figure formed by different parts when viewed from different angles of different vehicles, and by extracting the lines or figures to form a large number of graphic templates, the graphic template has one or more nodes, that is, the graphic template and This node is associated.

Step S5: Acquire and output the vehicle position signal and the vehicle layout relationship in the to-be-detected picture according to the position parameter corresponding to the hierarchical key nodes in the calculation branch and the graphic template.

In this embodiment, if the location parameter corresponding to the key nodes of each level (that is, the specific location of each part in the picture to be detected) and the corresponding graphic template are obtained, the graphic corresponding to the key nodes of each level may be obtained. The template is placed at a position corresponding to the position parameter, and finally the vehicle position signal and the vehicle layout relationship in the image to be detected are obtained, that is, the specific position of each vehicle and multiple vehicles are obtained (when there are multiple vehicles for the picture to be detected) Talk about the layout relationship between.

Compared with the prior art, the first embodiment of the present invention firstly processes the to-be-detected picture containing the vehicle signal to obtain a basic feature signal, and then inputs it into the pre-trained And-Or model to obtain key nodes at each level, and each will be After the hierarchical key nodes are associated, as a better calculation branch, for each calculation branch, after obtaining the graphic templates of the key nodes of each level and transforming the position parameters of the key nodes of each level, the positions corresponding to the key nodes of each level can be selected. The parameter and graphic template obtain the vehicle position signal and the vehicle layout relationship. In this embodiment, the And-Or model is used to detect and identify the vehicle, and the picture with complex scene can be processed, and the vehicle signal in the picture is effectively recognized and recognized efficiently. .

In a preferred embodiment, please refer to FIG. 1 and FIG. 2 simultaneously. In addition to the embodiment of FIG. 1 above, the above step S2 includes the following steps.

Step S21: input the basic feature signal into the And-Or model generated by the pre-training, and acquire a vehicle global area, where the vehicle global area is represented by the Or node and is the root node of the And-Or model.

Step S22: At the vehicle communication area level, each vehicle communication area is decomposed based on the root node, and the respective vehicle communication areas are respectively represented by different And nodes.

In step S23, at the level of the distribution location area of each of the vehicles, the area corresponding to each vehicle is extracted from the respective vehicle communication areas, and the area corresponding to each vehicle is represented by an Or node.

In step S24, the local components in the interior of the vehicle form an area level, and each partial component area of each vehicle is represented by an And node and organized.

In step S25, each Or node and each And node are output as key nodes.

In this embodiment, the above-mentioned hierarchy includes at least a vehicle communication area level, a distribution position area level of each vehicle, and a partial level of each part component in the vehicle as an example for description. Entering the basic feature signal into the pre-trained And-Or model may obtain the vehicle global area, that is, an area formed by an area corresponding to all vehicles in the picture to be detected, and the vehicle global area is an Or node. Represents and acts as the root node of the And-Or model.

At the level of the vehicle communication area, the respective vehicle communication areas are decomposed based on the root node, for example, the communication area between the first vehicle and the second vehicle is decomposed until the vehicle communication areas of all the vehicles are decomposed, and the vehicle communication areas are separated. Represented by different And nodes respectively.

At the level of the distribution location of each vehicle, the area corresponding to each vehicle is extracted through the above-mentioned vehicle communication area decomposed at the level of the communication area of the vehicle, so as to extract the area where each vehicle is located, each The area corresponding to the car is represented by the Or node.

After extracting the area corresponding to each vehicle, the local parts in the vehicle form an area level, and each part area of each vehicle is represented and organized by And nodes. Finally, each Or node and each And node are output as key nodes.

In a preferred embodiment, please refer to FIG. 1 and FIG. 3 simultaneously. On the basis of the embodiment of FIG. 1 above, before the step S2, the method further includes the following steps:

Step S01, acquiring a preset number of vehicle images with vehicle signals as training sample pictures of the And-Or model;

Step S02, extracting a preset proportion of the training sample picture as a training set, and using the remaining training sample picture as a test set, and setting a vehicle connection area and each vehicle for each training sample picture frame in the training set. a distribution location area and a partial component area of the interior of the vehicle;

Step S03, training the And-Or model by using the training sample picture after the frame processing, to train and generate an And-Or model for performing picture detection.

In step S04, each training sample picture in the test set is input into the And-Or model generated by the training to perform testing. If the accuracy of the test is greater than or equal to a preset threshold, the training ends.

In this embodiment, before training to generate the And-Or model, a preset number of vehicle images with vehicle signals are acquired as training sample pictures of the And-Or model, for example, 500,000 training sample pictures. A training sample picture of a preset proportion in the training sample picture is extracted as the training set, for example, 70% of the training sample pictures are extracted as the training set, and the remaining 30% is used as the test set. In the training, firstly, each training sample picture frame of the training set defines the communication area of the vehicle, the distribution position area of each vehicle, and the partial component composition areas inside the vehicle, and then the training after the frame processing is used. The sample picture trains the And-Or model. In the process, the And-Or model mainly acquires and learns the vehicle signal from three aspects: the first is to learn the context of the vehicle space layout according to the framed signal, and the second is to learn according to the framed signal. The occlusion relationship of the vehicle, and the third is to learn the visible part of the vehicle according to the framed signal. After training to generate the And-Or model, each training sample picture in the test set is input into the training-generated And-Or model for testing to test the accuracy. If the accuracy of the test is greater than or equal to a preset threshold, for example, greater than or equal to 0.95, the training is successful, and the training operation ends, and the And-Or model generated by the training can be used as a follow-up.

Preferably, on the basis of the embodiment shown in FIG. 3, after the step S04, the method further includes: if the accuracy of the test is less than the preset threshold, prompting to increase the number of the training sample picture, and returning to Step S02 and loop back.

In this embodiment, if each training sample picture in the test set is input into the And-Or model generated by the training, and the accuracy of the test is greater than a preset threshold, for example, less than 0.95, the training sample picture needs to be added. The number of the training samples, that is, the training sample and the training sample picture of the test set, for example, may be sent to the predetermined terminal to prompt the increase of the number of the training sample pictures, returning to step S02, and re-training until the test The accuracy rate is greater than or equal to the preset threshold.

Referring to FIG. 4, FIG. 4 is a schematic structural view of an embodiment of a device for detecting a vehicle according to the present invention. The device for detecting the vehicle includes the following structure.

An extraction module 101 is configured to extract a basic feature signal of the to-be-detected image by using a predetermined algorithm after receiving the to-be-detected picture including the vehicle signal.

The device for detecting a vehicle of the present embodiment can be applied to fields such as traffic safety monitoring, automobile production, and automobile insurance in a complicated scene, and a device for detecting a vehicle having a picture capturing function captures a picture in these scenes when captured. After the picture of the vehicle signal, the picture is taken as the picture to be detected, and the basic feature signals are extracted by some predetermined algorithms.

In this embodiment, the predetermined algorithm is some basic algorithms of image processing, such as an image edge detection algorithm, and the basic feature signal is a picture signal that can be directly input to the And-Or model, such as an image. The location or relationship of each part. Preferably, the present embodiment can obtain a gradient edge signal of the image to be detected by using a Histogram of Oriented Gradient (HOG) algorithm, and then use a K-means clustering algorithm to obtain each gradient edge. The clustering center of the image or the DPM (Deformable Parts Model) algorithm is used to obtain the mutual positional relationship of each part of the image after the gradient edge.

A training module 102 is configured to input the basic feature signal into the pre-trained And-Or model to acquire each hierarchical node through the pre-trained And-Or model, and use the acquired hierarchical nodes as a key Node output.

In the embodiment, the And-Or model is obtained by training a plurality of pictures containing the vehicle signal in advance, and inputting the extracted basic feature signals into the pre-trained And-Or model, and adopting the advance The And-Or model generated by the training learns the input basic feature signal. In the learning process, the root node is first obtained, and then the nodes corresponding to the respective levels can be obtained based on the root node, and then the nodes corresponding to the respective levels are used as the key. Node output.

In the And-Or model generated by the pre-training in this embodiment, preferably, the above-mentioned hierarchy includes at least three, that is, a level of a vehicle communication area, a distribution level of each vehicle, and a part of a vehicle interior. Regional level. Of course, the level can also be less than three or more than three.

An association module 103 is configured to associate the output key nodes to use the associated hierarchical key nodes as a preferred calculation branch.

In this embodiment, after the key node is output, the output key node is associated. The key nodes may be associated with each other according to the root node. Specifically, the key nodes in each level may be associated first, for example, the location relationships of the key nodes in the same level are associated. Determining the relative positions of the key nodes in the same level; then, associating the key nodes of each level according to the positional relationship, for example, associating the positions of the key nodes in different levels to determine the different levels in the hierarchy The relative position of each key node, after the key nodes are associated, the architecture of each part of the image to be detected can be outlined, and then the associated key nodes of each level are used as the pre-trained And-Or model in the learning process. A good calculation branch for the next step.

a conversion module 104 is configured to convert the hierarchical key nodes in the calculation branch into position parameters in the to-be-detected picture, and determine the calculation branch according to the predetermined association relationship between the hierarchical key nodes and the graphic template. The graphic template corresponding to the key nodes in the hierarchy.

In this embodiment, the key nodes in the preferred calculation branch are converted into position parameters in the to-be-detected picture to obtain a specific location of each part in the to-be-detected picture.

In addition, for each level in each of the preferred calculation branches, a graphic template corresponding to each of the hierarchical key nodes may be determined according to the relationship between the predetermined hierarchical key nodes and the graphic template, for example, a key node of a certain level If it is an ellipse, the associated graphic template is an ellipse. The graphic template is a line or a figure formed by different parts when viewed from different angles of different vehicles, and the drawing pattern has a plurality of graphic templates by extracting the lines or graphics, and the graphic template has one or more nodes, that is, a graphic template and a node. Associated.

An output module 105 is configured to acquire and output a vehicle position signal and a vehicle layout relationship in the image to be detected according to the position parameter corresponding to the hierarchical key nodes in the calculation branch and the graphic template.

In this embodiment, if the location parameter corresponding to the key nodes of each level is obtained (that is, the specific location of each part in the picture to be detected is obtained) and the corresponding graphic template, the corresponding key node of each level may be The graphic template is placed at a position corresponding to the position parameter, and finally the vehicle position signal and the vehicle layout relationship in the image to be detected are obtained, that is, the specific position of each vehicle and multiple vehicles are obtained (for when there are multiple vehicles for the image to be detected) The relationship between the layout.

In a preferred embodiment, based on the embodiment of FIG. 4 above, the training module 102 includes the following structure.

An acquiring unit is configured to input the basic feature signal into the pre-trained And-Or model, and acquire a vehicle global area, where the vehicle global area is represented by an Or node and is a root node of the And-Or model.

A decomposing unit is configured to decompose each vehicle communication area based on the root node at the vehicle communication area level, and the respective vehicle communication areas are respectively represented by different And nodes.

An extraction unit is configured to extract an area corresponding to each vehicle from the communication area of each vehicle at a level of the distribution location area of each of the vehicles, and an area corresponding to each vehicle is represented by an Or node.

An organization unit for each regional component in the interior of the vehicle constitutes a regional level, and each local component region of each vehicle is represented and organized by an And node.

An output unit is configured to output each Or node and each And node as a key node.

In the embodiment, the above-mentioned hierarchy includes at least a vehicle communication area level, a distribution position area level of each vehicle, and each local component composition area level inside the vehicle as an example for description. Entering the basic feature signal into the pre-trained And-Or model may acquire a vehicle global area, that is, an area formed by an area including all the vehicles in the to-be-detected picture, and the vehicle global area is an Or node. Represents and acts as the root node of the And-Or model.

At the level of the vehicle communication area, the respective vehicle communication areas are decomposed based on the root node, for example, the communication area between the first vehicle and the second vehicle is decomposed, until the vehicle communication areas of all the vehicles are decomposed, and the vehicles are connected. The regions are represented by different And nodes.

At the level of the distribution location area of each of the vehicles, the area corresponding to each vehicle is extracted through the above-mentioned vehicle communication area decomposed at the level of the communication area of the vehicle, so as to extract the area where each vehicle is located, each of which is located The area corresponding to a car is represented by an Or node.

After extracting the area corresponding to each vehicle, the local parts in the vehicle form an area level, and each part area of each vehicle is represented and organized by And nodes. Finally, each Or node and each And node are output as key nodes.

In a preferred embodiment, please refer to FIG. 4 and FIG. 5 simultaneously. Based on the embodiment of FIG. 4 above, the device for detecting a vehicle further includes the following structure.

An acquisition module 201 is configured to acquire a preset number of vehicle images with vehicle signals as training sample images of the And-Or model, and a frame module 202 for extracting a preset proportion of the training sample images as training The set is taken as the test set, and each training sample picture frame in the training set defines a vehicle communication area, a distribution position area of each vehicle, and a partial component composition area inside the vehicle.

a generating module 203 is configured to train the And-Or model by using the training sample picture after the frame processing, to generate an And-Or model for performing image detection, and a test module 204 for testing the test Each training sample picture in the set is input into the And-Or model generated by the training for testing, and if the accuracy of the test is greater than or equal to a preset threshold, the training ends.

In this embodiment, before the training generates the And-Or model, a preset number of vehicle images with vehicle signals are acquired as training sample images of the And-Or model, for example, 500,000 training sample pictures. A training sample picture of a preset proportion in the training sample picture is extracted as the training set, for example, 70% of the training sample pictures are extracted as the training set, and the remaining 30% is used as the test set. In the training, firstly, each training sample picture frame of the training set defines the communication area of the vehicle, the distribution position area of each vehicle, and the partial component composition areas inside the vehicle, and then the training after the frame processing is used. The sample picture trains the And-Or model. In the process, the And-Or model mainly acquires and learns the vehicle signal from three aspects: the first is to learn the context of the vehicle space layout according to the framed signal, and the second is to frame the signal according to the frame. Learning the occlusion relationship of the vehicle, the third is to learn the visible part of the vehicle according to the framed signal. After training to generate the And-Or model, each training sample image in the test set is input into the training-generated And-Or model for testing to test the accuracy. If the accuracy of the test is greater than or equal to a preset threshold, for example, greater than or equal to 0.95, the training is successful, and the training operation ends, and the And-Or model generated by the training can be used as a follow-up.

Preferably, the apparatus for detecting a vehicle further includes: an adding module, if the accuracy of the test is less than a preset threshold, prompting to increase the number of the training sample picture. For example, by sending a prompt signal to the predetermined terminal to prompt to increase the number of the training sample picture, the frame module 202 is triggered to continue training to generate an And-Or model for performing picture detection.

In this embodiment, if each training sample picture in the test set is input into the And-Or model generated by the training, and the accuracy of the test is less than a preset threshold, for example, less than 0.95, the training sample picture needs to be added. The number of the training set and the training sample picture of the test set are added, and then the frame module 202 is triggered to re-train until the accuracy of the test is greater than or equal to a preset threshold.

The above is only the disclosure of the preferred embodiments of the present invention, and is not intended to limit the present invention. Any modifications, equivalents, improvements, etc., which are included in the spirit and scope of the present invention, should be included in the present invention. Within the scope of protection of the invention.

Claims (10)

 A method for detecting a vehicle, comprising: S1, after receiving a to-be-detected picture including a vehicle signal, extracting a basic characteristic signal of the to-be-detected picture by using a predetermined algorithm; S2, inputting the basic characteristic signal into a pre-trained generated image In the And-Or model, each hierarchical node is obtained by using the And-Or model generated by the pre-training, and the obtained hierarchical nodes are output as key nodes; and S3, the outputted key nodes are associated to associate the selected nodes. The key nodes of each level are used as a better calculation branch; S4, the key nodes of the hierarchy in the calculation branch are converted into position parameters in the image to be detected, and according to the predetermined association between the key nodes of the hierarchy and the graphic template The relationship determines the graphic template corresponding to the key nodes of each level in the calculation branch; and S5, acquiring the vehicle in the image to be detected according to the position parameter corresponding to the key nodes of the hierarchical level in the calculation branch and the graphic template Position signal and vehicle layout relationship and output.    The method of vehicle detection according to claim 1, wherein the hierarchy comprises at least three of: a vehicle communication area level, a distribution position area level of each vehicle, and a partial level of each part of the vehicle.    The method of vehicle detection according to claim 2, wherein the step S2 comprises: S21, inputting the basic feature signal into the pre-trained And-Or model, and acquiring a vehicle global area, the vehicle global domain The area is represented by the Or node and serves as the root node of the And-Or model; S22, at the level of the vehicle communication area, the respective vehicle communication areas are decomposed based on the root node, and the respective vehicle communication areas are respectively represented by different And nodes; S23 At the level of the distribution location of each of the vehicles, the area corresponding to each vehicle is extracted from the communication area of each vehicle, and the area corresponding to each vehicle is represented by an Or node; S24, each inside the vehicle The local components constitute an area hierarchy, and the respective local component areas of each vehicle are respectively represented and organized by the And node; and S25, each Or node and each And node are output as key nodes.    The method of vehicle detection according to any one of claims 1 to 3, wherein before the step S2, the method further comprises: S01, acquiring a preset number of vehicle images with vehicle signals as the And-Or model a training sample picture; S02, extracting a preset ratio of the training sample picture as a training set, and using the remaining training sample picture as a test set, and determining a vehicle connected area for each training sample picture frame in the training set, The distribution location area of each vehicle and each local component of the vehicle constitute an area; S03, training the And-Or model by using the training sample picture after the frame processing, to train and generate an And-Or model for image detection And S04, each training sample picture in the test set is input into the training-generated And-Or model for testing, and if the accuracy of the test is greater than or equal to a preset threshold, the training ends.    The method of vehicle detection according to claim 4, wherein after the step S04, the method further comprises: if the accuracy of the test is less than a preset threshold, prompting to increase the number of the training sample pictures.    The device for detecting a vehicle includes: an extraction module, configured to: after receiving a to-be-detected image including a vehicle signal, extract a basic feature signal of the image to be detected by using a predetermined algorithm; a training module is configured to The basic feature signal is input into the And-Or model generated by the pre-training to acquire each hierarchical node through the pre-trained And-Or model, and the acquired hierarchical nodes are output as key nodes; an associated module, The function is used to associate the key nodes of the output to use the associated hierarchical key nodes as a preferred calculation branch; a conversion module is configured to convert the hierarchical key nodes in the calculation branch into the to-be-detected a position parameter in the picture, and determining, according to the predetermined association relationship between the key nodes of each level and the graphic template, the graphic template corresponding to the key nodes of each level in the calculation branch; and an output module for using the calculation according to the calculation The position parameter corresponding to the key nodes of each level in the branch and the graphic template obtain the vehicle position signal in the image to be detected and Vehicle layout relationship and output.    The apparatus for vehicle detection according to claim 6, wherein the hierarchy includes at least three of: a vehicle communication area level, a distribution position area level of each vehicle, and a partial level of each part of the vehicle.    The device for detecting a vehicle according to claim 7 , wherein the training module comprises: an acquiring unit, configured to input the basic feature signal into the pre-trained And-Or model, and acquire a vehicle global domain a region, the vehicle global region is represented by an Or node and serves as a root node of the And-Or model; a decomposition unit is configured to, at the vehicle communication region level, decompose each vehicle communication region based on the root node, the respective vehicle connectivity regions Respectively represented by different And nodes; an extraction unit for extracting the area corresponding to each vehicle from the respective vehicle communication areas at each of the distribution location areas of each vehicle, corresponding to each vehicle The area is represented by the Or node; an organization unit for each regional component inside the vehicle to form an area level, each local component area of each vehicle is represented and organized by the And node; and an output unit, It is used to output each Or node and the And nodes as key nodes.    The device for detecting a vehicle according to any one of claims 6 to 8, further comprising: an acquisition module, configured to acquire a preset number of vehicle images with vehicle signals as the And-Or model The training sample picture; a frame setting module, configured to extract a preset proportion of the training sample picture as a training set, and use the remaining training sample picture as a test set, and frame each training sample picture in the training set a communication area of the vehicle, a distribution location area of each vehicle, and a partial component area of the interior of the vehicle; a generation module for training the And-Or model by using the training sample picture after the frame processing, to generate the training The And-Or model for performing image detection; and a test module for inputting each training sample image in the test set into the training-generated And-Or model for testing, if the accuracy of the test If the threshold is greater than or equal to the preset threshold, the training ends.    The device for detecting a vehicle according to claim 9 , wherein the device further comprises: an adding module, if the accuracy of the test is less than a preset threshold, prompting to increase the number of the training sample picture, triggering the frame setting The module continues training to generate the And-Or model for image detection.