Target detection method, computer device, computer readable storage medium and vehicle

Abstract

The present application relates to the technical field of target detection, and particularly relates to a target detection method, computer equipment, a storage medium and a vehicle, and aims to solve the problem of quickly and accurately detecting multiple targets. To this end, the method comprises the following steps: extracting image features of a to-be-detected image through a feature extraction network of a multi-target detection model; detecting different types of targets respectively through each detection head network according to the image features; the feature extraction network comprises a feature pyramid down-sampling network and multiple feature pyramid up-sampling networks, the feature pyramid down-sampling network and each feature pyramid up-sampling network are connected through a transverse connection network respectively, each feature pyramid up-sampling network is connected with at least one detection head network and the detection head networks connected with the same feature pyramid up-sampling network detect targets of the same shape type. In the above manner, multiple types of targets can be detected quickly and accurately.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic driving, and in particular to a target detection method, a computer device, a computer readable storage medium and a vehicle. BACKGROUND

[0002] In the process of controlling vehicle automatic driving, it is necessary to accurately detect targets such as motor vehicles, non-motor vehicles, pedestrians, traffic signs and lane lines around the vehicle, and then plan trajectories according to these targets to ensure the safe driving of the vehicle. The conventional method for detecting the above targets is mainly to use neural network technology to construct a corresponding target detection model (single task model) for each type of target, and then use different single task models to detect different types of targets. However, deploying and running multiple single task models on mobile devices such as vehicle machines will put a lot of pressure on the computing resources and power consumption of the vehicle machine. To this end, a neural network technology can be used to construct a target detection model (multi-task model) that can simultaneously detect multiple targets, to reduce the pressure on the computing resources and power consumption of the mobile device. However, in the above multi-task model, the target detection tasks of each target are shared the same image features for target detection, which results in the detection accuracy of each target by the multi-task model being lower than the detection accuracy of the corresponding single task model. If the target detection results of such a multi-task model are used to plan trajectories for the vehicle, it may be difficult to ensure the safe driving of the vehicle.

[0003] Correspondingly, there is a need in the art for a new technical solution to solve the above problems. SUMMARY

[0004] In order to overcome the above defects, the present application is proposed to provide a target detection method, a computer device, a computer readable storage medium and a vehicle that solve or at least partially solve the technical problem of how to quickly and accurately detect multiple targets such as motor vehicles, non-motor vehicles, pedestrians, traffic signs and lane lines.

[0005] In a first aspect, a target detection method is provided, the method comprising:

[0006] obtaining a to-be-detected image;

[0007] extracting image features of the to-be-detected image through a feature extraction network of a multi-target detection model and inputting the image features into each detection head network of the multi-target detection model;

[0008] detecting different types of targets through each detection head network and according to the image features, respectively;

[0009] The feature extraction network comprises a feature pyramid down-sampling network and a plurality of feature pyramid up-sampling networks, the feature pyramid down-sampling network is connected to each of the feature pyramid up-sampling networks through a horizontal connection network, each of the feature pyramid up-sampling networks is connected to at least one of the detection head networks, and the detection head networks connected to the same feature pyramid up-sampling network detect targets of the same shape type.

[0010] In one of the technical solutions of the target detection method, the method further comprises classifying the shape types of the targets detected by the detection head networks in the multi-target detection model when the multi-target detection model is constructed by the following method:

[0011] Determining target image samples used for model training of the multi-target detection model;

[0012] According to the shape of the labeled label of the target sample on the target image sample, the shape types of the targets detected by the detection head networks in the multi-target detection model are classified.

[0013] In one of the technical solutions of the target detection method, the step of "obtaining the to-be-detected image" specifically comprises:

[0014] Image acquisition is performed by a high-precision image acquisition device to obtain the to-be-detected image.

[0015] In one of the technical solutions of the target detection method, the method further comprises model training of the multi-target detection model by the following method:

[0016] The knowledge distillation method is used to train the model of the multi-target detection model.

[0017] In one of the technical solutions of the target detection method, the step of "using the knowledge distillation method to train the model of the multi-target detection model" specifically comprises:

[0018] A plurality of first teacher models capable of detecting different types of targets are obtained;

[0019] The knowledge distillation method is used to guide each of the first teacher models to train the model of the multi-target detection model using target image samples.

[0020] In one of the technical solutions of the target detection method, the target detection method is applied to a mobile device, and the step of "using the knowledge distillation method to train the model of the multi-target detection model" specifically comprises:

[0021] A plurality of second teacher models capable of detecting different types of targets are obtained;

[0022] The knowledge distillation method is used to guide the second teacher model to guide the multi-target detection model to perform model training using target image samples.

[0023] In one of the technical solutions of the target detection method, the shape types of the targets detected by the detection head network include at least a rectangle and a line.

[0024] In a second aspect, a computer device is provided, which includes a processor and a storage device, the storage device being adapted to store a plurality of program codes, the program codes being adapted to be loaded and run by the processor to perform the target detection method in any one of the technical solutions of the target detection method described above.

[0025] In a third aspect, a computer readable storage medium is provided, which has a plurality of program codes stored therein, the program codes being adapted to be loaded and run by a processor to perform the target detection method in any one of the technical solutions of the target detection method described above.

[0026] In a fourth aspect, a vehicle is provided, which includes the computer device in the technical solution of the computer device described above.

[0027] The one or more technical solutions of the application have at least one or more of the following beneficial effects:

[0028] In the implementation of the technical solutions of the application, an image to be detected can be acquired, image features of the image to be detected are extracted by a feature extraction network of a multi-target detection model and input into each detection head network of the multi-target detection model, different types of targets are detected by each detection head network and according to the image features; wherein the feature extraction network can include a feature pyramid down-sampling network and a plurality of feature pyramid up-sampling networks, the feature pyramid down-sampling network and each feature pyramid up-sampling network are connected by a horizontal connection network respectively, each feature pyramid up-sampling network is connected with at least one detection head network and the detection head networks connected with the same feature pyramid up-sampling network detect targets of the same shape type.

[0029] Through the above implementation, only one multi-target detection model needs to be deployed on the device, and target detection of multiple different types of targets can be performed, thereby reducing the pressure on the computing resources and power consumption of the device. At the same time, through the way that the detection head networks connected with the same feature pyramid up-sampling network detect targets of the same shape type, the detection head networks detecting targets of the same shape type can share the same image features for target detection, thereby improving the detection accuracy of these targets of the same shape type and avoiding the interference of target detection of targets of different shape types. BRIEF DESCRIPTION OF DRAWINGS

[0030] The disclosure of the present application will become more fully understood from the detailed description given herein below, and appended claims, accompanied by the accompanying drawings. It is to be understood that the drawings are only for the purpose of illustration:

[0031] Figure 1 is a schematic diagram of the main steps of a target detection method according to an embodiment of the present application;

[0032] Figure 2 is a schematic diagram of the connection of a feature pyramid down-sampling network and a feature pyramid up-sampling network;

[0033] Figure 3 is a schematic diagram of the application scenario of a target detection method according to an embodiment of the present application;

[0034] Figure 4 is a schematic diagram of the main steps of a method for classifying the shape type of a target detected by a detection head network in a multi-target detection model according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] Some embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art will understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0036] In the description of the present application, the "processor" can include hardware, software or a combination of both. The processor can be a central processor, a microprocessor, an image processor, a digital signal processor or any other suitable processor. The processor has data and / or signal processing functions. The processor can be implemented in software, hardware or a combination of both. The non-transitory computer readable storage medium includes any suitable medium that can store program code, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, etc.

[0037] Referring to the drawings, Figure 1 , Figure 1 is a schematic diagram of the main steps of a target detection method according to an embodiment of the present application. As shown in Figure 1 , the target detection method in the embodiment of the present application mainly includes the following steps S101 to S103.

[0038] Step S101: Obtain an image to be detected.

[0039] In this embodiment, an image acquisition device can be used to acquire an image to be detected. However, in some implementations, to improve the accuracy of target detection, a high-precision image acquisition device can be used to acquire the image to be detected. The image to be detected acquired by the high-precision image acquisition device can accurately detect not only nearby targets but also distant targets.

[0040] Image precision refers to image resolution. A high-precision image is an image with a resolution greater than a preset resolution threshold, where the preset resolution threshold is 720p. A high-precision image acquisition device is an image acquisition device that acquires images that are high-precision. In some preferred embodiments, the high-precision image acquired by the high-precision image acquisition device has a resolution of 4k.

[0041] Step S102: Extract image features of the image to be detected through the feature extraction network of the multi-object detection model and input the image features into each detection head network of the multi-object detection model.

[0042] In this embodiment, the multi-target detection model includes at least a feature extraction network and multiple detection head networks. The feature extraction network can be configured to extract image features from the input image, and each detection head network can be configured to detect different types of targets based on the image features extracted by the feature extraction network, thereby obtaining information such as the type and location of the corresponding targets.

[0043] The feature extraction network may include a feature pyramid downsampling network and multiple feature pyramid upsampling networks. The feature pyramid downsampling network is connected to each feature pyramid upsampling network through a lateral connection network.

[0044] See appendix Figure 2 , Figure 2 An exemplary embodiment illustrates the connection structure between a feature pyramid downsampling network and a feature pyramid upsampling network via a lateral connection network. In this embodiment, each feature pyramid upsampling network employs a connection with... Figure 2 The same connection structure is shown, with the feature pyramid downsampling network connected to the feature pyramid upsampling network via a lateral connection network. It should be noted that the network structures of the feature pyramid upsampling network, feature pyramid downsampling network, and lateral connection network are all conventional network structures in the field of feature pyramid network technology. This embodiment of the invention does not limit the specific network structures of the feature pyramid upsampling network, feature pyramid downsampling network, and lateral connection network. Furthermore, it should be noted that those skilled in the art can use conventional head networks in the field of target detection technology to construct head networks for detecting different types of targets, and this embodiment of the invention also does not specifically limit this.

[0045] Furthermore, each feature pyramid upsampling network is connected to at least one detection head network, and the detection head networks connected to the same feature pyramid upsampling network detect targets of the same shape type. In other words, detection head networks that detect targets of the same shape type in the multi-object detection model are connected to the same feature pyramid upsampling network and share the image features output by this feature pyramid upsampling network.

[0046] The target shapes detected by the detection head network include at least rectangular and linear shapes. For example, in a multi-target detection model, detection head networks that detect rectangular targets can be connected to a feature pyramid upsampling network, while detection head networks that detect linear targets can be connected to another feature pyramid upsampling network.

[0047] In some implementations, if the number of detection head networks that detect targets with the same shape type in a multi-target detection model is large, the targets can be further divided according to their motion / static type. This ensures that the target shape type and motion / static type detected by the detection head networks connected to the same feature pyramid sampling network are identical. The motion / static type of the target includes both dynamic and static targets. For example, vehicles, people, and non-motorized vehicles are dynamic targets, while traffic signs, traffic lights, and lane lines are static targets.

[0048] like Figure 3 As shown, in some application scenarios of this invention, the targets include six types: vehicles, pedestrians, non-motorized vehicles, traffic lights, traffic signs, and lane lines. Vehicles, pedestrians, non-motorized vehicles, traffic lights, and traffic signs are all rectangular in shape, while lane lines are linear. The multi-target detection model can include a feature extraction network and six detection head networks (vehicle detection head network, pedestrian detection head network, non-motorized vehicle detection head network, traffic light detection head network, traffic sign detection head network, and lane line detection head network). The feature extraction network includes three feature pyramid upsampling networks: one connected to the vehicle detection head network, the pedestrian detection head network, and the non-motorized vehicle detection head network; one connected to the traffic light detection head network and the traffic sign detection network; and one connected to the lane line detection head network. The vehicle detection head network, pedestrian detection head network, and non-motorized vehicle detection head network share the same image features to detect vehicles, pedestrians, and non-motorized vehicles respectively. The traffic light detection head network and traffic sign detection head network share the same image features to detect traffic lights and traffic signs respectively. The lane line detection head network does not share image features with other detection head networks.

[0049] Step S103: detecting different types of targets respectively through each detection head network according to the image features.

[0050] In the embodiment, the detection head network can detect which types of targets in the image to be detected and the positions of the targets and the like according to the image features.

[0051] Based on the method described in steps S101 to S103, the detection head networks detecting targets of the same shape type in the multi-target detection model can share the same image features, so as to improve the detection accuracy of the targets of the same shape type and avoid the detection interference of targets of different shape types.

[0052] The multi-target detection model in the embodiment will be further described below.

[0053] In the embodiment, the model structure of the multi-target detection model can be constructed first, and then the model training of the multi-target detection model is performed after the model structure is constructed, so as to obtain the trained multi-target detection model, and then the target detection method described in steps S101 to S103 can be performed by using the trained multi-target detection model.

[0054] I. Model construction of multi-target detection model

[0055] In the embodiment, the shape types of the targets detected by the detection head network can be classified when the model structure of the multi-target detection model is constructed, so as to determine the number of the detection head networks in the multi-target detection model and the shape types of the targets detected by each detection head network. Specifically, refer to FIG. 1, which is a schematic diagram of the model structure of the multi-target detection model in the embodiment. Figure 4 In the embodiment, the shape types of the targets detected by the detection head network in the multi-target detection model can be classified through the following steps S201 to S202.

[0056] Step S201: determining the target image samples used for model training of the multi-target detection model.

[0057] The target image sample refers to an image containing a target sample, and each target image sample contains a labeled label of a corresponding target sample. The labeled label at least contains the position information of the target sample on the target image sample, and the shape of the labeled label is similar to the shape of the target sample, that is, the shape types of the two are the same. For example, if the target sample is a person, the labeled label is a circumscribed rectangle frame of the person, and the shape types of the person and the labeled label are both rectangles.

[0058] Step S202: classifying the shape types of the targets detected by the detection head network in the multi-target detection model according to the shape of the labeled label of the target sample on the target image sample.

[0059] In the embodiment, firstly, it can be determined according to the shape of the annotation label of the target sample on the target image sample which shape types exist; then, the number of detection head networks in the multi-target detection model is determined according to the number of target types (such as the six types of vehicles, people, non-motor vehicles, traffic lights, traffic signs and lane lines); finally, the shape type of the target detected by each detection head network is determined, that is, the classification of the shape type of the target detected by the detection head network in the multi-target detection model is completed.

[0060] For example, it is assumed that the target image samples used for model training of the multi-target detection model include target image samples of six types of target samples, i.e., vehicles, people, non-motor vehicles, traffic lights, traffic signs and lane lines. According to the shape of the annotation label of the target sample on these target image samples, it can be determined that there are two shape types, i.e., rectangular and linear, and it can also be determined that six detection head networks, i.e., vehicle detection head network, person detection head network, non-motor vehicle detection head network, traffic light detection head network, traffic sign detection head network and lane line detection head network, need to be set. The shape types of the targets detected by the six detection head networks, i.e., vehicle detection head network, person detection head network, non-motor vehicle detection head network, traffic light detection head network, traffic sign detection head network and lane line detection head network, are rectangular, rectangular, rectangular, rectangular, rectangular and linear, respectively. Finally, according to the principle that the shape types of the targets detected by the detection head networks connected by the same feature pyramid upsampling network are the same, the vehicle detection head network, the person detection head network, the non-motor vehicle detection head network, the traffic light detection head network and the traffic sign detection head network with the shape type of rectangular are connected to one feature pyramid upsampling network, and the lane line detection head network with the shape type of linear is connected to another feature pyramid upsampling network.

[0061] Through the method described in steps S201 to S202, the shape types of the targets detected by different detection head networks can be accurately divided according to the detection capabilities of each type of target required by the multi-target detection model, so that the detection head networks with the same shape type of the detected targets can share the same image features in the manner that the shape types of the targets detected by the detection head networks connected by the same feature pyramid upsampling network are the same, thereby improving the detection accuracy of these targets with the same shape type and avoiding the detection interference of other targets with different shape types.

[0062] II. Model training of the multi-target detection model

[0063] In the embodiment of the application, the knowledge distillation method (Knowledge Distillation) can be used to train the multi-target detection model.

[0064] The knowledge in the trained teacher model can be distilled into the multi-target detection model to be trained by a knowledge distillation method, so as to obtain the trained multi-target detection model.

[0065] In some embodiments, the trained teacher model can be composed of a plurality of first teacher models capable of detecting different types of targets respectively, that is, in the present embodiment, a plurality of first teacher models capable of detecting different types of targets respectively are used to train the multi-target detection model to be trained, so as to obtain the trained multi-target detection model. Specifically, in the present embodiment, the multi-target detection model to be trained can be trained by the following steps 11 to 12.

[0066] Step 11: Obtain a plurality of first teacher models capable of detecting different types of targets respectively.

[0067] In the present embodiment, each first teacher model has high target detection accuracy, that is, has high target detection capability. In some embodiments, a first teacher model can be constructed using a large-scale model structure to improve the learning ability of the first teacher model, and thus improve the target detection capability of the first teacher model. That is, compared with the multi-target detection model to be trained, the model structure of the first teacher model is more complex and consumes more computing resources.

[0068] Step 12: Use a knowledge distillation method to guide each first teacher model to train the multi-target detection model using target image samples respectively.

[0069] The knowledge in each first teacher model can be distilled into the multi-target detection model to be trained by a knowledge distillation method, so as to obtain the trained multi-target detection model.

[0070] It should be noted that those skilled in the art can use a conventional knowledge distillation method in the field of machine learning to guide each first teacher model to train the multi-target detection model using target image samples respectively, and the present embodiment does not elaborate on the specific principles and methods of the knowledge distillation method.

[0071] By the above steps 11 to 12, the multi-target detection model to be trained can be trained by using a plurality of first teacher models, so that the multi-target detection model has high detection accuracy for each type of target (the type of target that each first teacher model can detect).

[0072] In some other embodiments, the above-mentioned target detection method performed on the mobile device by using the multi-target detection model in steps S101-S103 can be implemented by the following steps S11-S12.

[0073] Step 21: Obtain a second teacher model capable of detecting multiple different types of targets respectively.

[0074] The second teacher model refers to a model constructed using a larger model structure and having higher target detection capability, i.e., compared with the multi-target detection model to be trained, the model structure of the second teacher model is more complex and consumes more computing resources, and if the second teacher model is deployed on the mobile device, it will bring great pressure to the computing resources and power consumption of the mobile device.

[0075] Step 22: Use a knowledge distillation method to guide the multi-target detection model to use the target image samples for model training.

[0076] Through the knowledge distillation method, the knowledge in the second teacher model can be distilled into the multi-target detection model to be trained, thereby obtaining the trained multi-target detection model.

[0077] It should be noted that those skilled in the art can use the conventional knowledge distillation method in the field of machine learning to guide the multi-target detection model to use the target image samples for model training, and the specific principles and methods of the knowledge distillation method are not described in detail in the embodiments of the present application.

[0078] Through the above steps S21-S22, the model training of the multi-target detection model to be trained can be completed by using a second teacher model, so that the multi-target detection model has high detection accuracy for each type of target (the types of targets that the second teacher model can detect).

[0079] It should be noted that although the above embodiments describe each step in a specific order, those skilled in the art can understand that, in order to achieve the effect of the present application, the different steps do not necessarily have to be executed in this order, they can be executed simultaneously (in parallel) or in other order, and these changes are within the protection scope of the present application.

[0080] Those skilled in the art can understand that all or part of the processes in the method of the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable storage medium can include any entity or device, medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code. It should be noted that the contents included in the computer readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable storage medium does not include electrical carrier signals and telecommunication signals.

[0081] Further, the present application also provides a computer device. In an embodiment of the computer device according to the present application, the computer device includes a processor and a storage device, the storage device can be configured to store a program for executing the target detection method of the above-mentioned method embodiments, and the processor can be configured to execute the program in the storage device, which includes but is not limited to the program for executing the target detection method of the above-mentioned method embodiments. For the convenience of illustration, only the parts related to the embodiments of the present application are shown, and the specific technical details not disclosed are referred to the method part of the embodiments of the present application. The computer device can be a control device formed by various electronic devices.

[0082] Further, the present application also provides a computer readable storage medium. In an embodiment of the computer readable storage medium according to the present application, the computer readable storage medium can be configured to store a program for executing the target detection method of the above-mentioned method embodiments, which can be loaded and run by the processor to implement the above-mentioned target detection method. For the convenience of illustration, only the parts related to the embodiments of the present application are shown, and the specific technical details not disclosed are referred to the method part of the embodiments of the present application. The computer readable storage medium can be a storage device formed by various electronic devices, and optionally, the computer readable storage medium in the embodiments of the present application is a non-transitory computer readable storage medium.

[0083] Further, the present application also provides a vehicle. In an embodiment of the vehicle according to the present application, the vehicle can comprise the computer device as described in the above embodiment of the computer device. In this embodiment, the vehicle can be an autonomous vehicle, a driverless vehicle, or the like. In addition, according to the type of power source, the vehicle in this embodiment can be a fuel vehicle, an electric vehicle, a hybrid vehicle using electric energy and fuel, or a vehicle using other new energy, and the like.

[0084] So far, the technical solutions of the present application have been described in combination with one embodiment shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will all fall within the protection scope of the present application.

Claims

1. A target detection method characterized by, The method comprises: acquiring an image to be detected; extracting image features of the image to be detected by a feature extraction network of a multi-target detection model and inputting the image features into each detection head network of the multi-target detection model; detecting different types of targets respectively by each detection head network according to the image features; wherein the feature extraction network comprises a feature pyramid down-sampling network and a plurality of feature pyramid up-sampling networks, the feature pyramid down-sampling network and each feature pyramid up-sampling network are connected by a horizontal connection network respectively, each feature pyramid up-sampling network is connected with at least one detection head network and the detection head networks connected with the same feature pyramid up-sampling network detect targets of the same shape type.

2. The object detection method of claim 1, wherein, The method further comprises classifying the shape types of the targets detected by the detection head networks in the multi-target detection model by the following method when the multi-target detection model is constructed: determining target image samples used for model training of the multi-target detection model; classifying the shape types of the targets detected by the detection head networks in the multi-target detection model according to the shapes of the annotation labels of the target samples on the target image samples.

3. The object detection method of claim 1, wherein, The step of "acquiring an image to be detected" specifically comprises: performing image acquisition by a high-precision image acquisition device to obtain the image to be detected.

4. The object detection method of claim 1, wherein, The method further comprises model training of the multi-target detection model by the following method: adopting a knowledge distillation method to perform model training of the multi-target detection model.

5. The object detection method of claim 4, wherein, The step of "adopting a knowledge distillation method to perform model training of the multi-target detection model" specifically comprises: acquiring a plurality of first teacher models capable of detecting different types of targets respectively; adopting a knowledge distillation method to make each first teacher model guide the multi-target detection model to perform model training using target image samples.

6. The object detection method of claim 4, wherein, The target detection method is applied to a mobile device, and the step of "adopting a knowledge distillation method to perform model training of the multi-target detection model" specifically comprises: acquiring a second teacher model capable of detecting a plurality of different types of targets respectively; adopting a knowledge distillation method to make the second teacher model guide the multi-target detection model to perform model training using target image samples.

7. The object detection method of claim 2, wherein, The shape types of the targets detected by the detection head network at least include rectangular and linear shapes.

8. A computer device comprising a processor and a storage device, said storage device being adapted to store a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by the processor to execute the target detection method of any one of claims 1 to 7.

9. A computer readable storage medium having stored therein a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by the processor to execute the target detection method of any one of claims 1 to 7.

10. A vehicle characterized by comprising: The vehicle comprises the computer device of claim 8.

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