Object detection method, electronic device, and computer readable storage medium

Abstract

The present application provides an object detection method, an electronic device, and a computer readable storage medium. The method comprises: acquiring an image parameter of an original image (S110), and then comparing the image parameter of the original image with a corresponding reference parameter range, so as to determine, on the basis of the comparison result, whether the original image satisfies a detection condition of an object detection model. In the embodiments of the present application, when the image parameter of the original image is out of the corresponding reference parameter range, it is considered that the original image does not satisfy the detection condition of the object detection model, and image enhancement processing is performed on the original image to obtain an enhanced image satisfying the detection condition of the object detection model (S120); and then the enhanced image is inputted into a pre-trained object detection model to obtain an object detection result (S130).

Description

Target detection method, electronic device, and computer-readable storage medium

[0001] Cross-references to related documents

[0002] This application is based on the Chinese patent application with application number 202311010316.4 and application date August 10, 2023, and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby introduced into this application as a reference. Technical Field

[0003] The present application relates to the field of artificial intelligence technology, and in particular to a target detection method, an electronic device, and a computer-readable storage medium. Background Art

[0004] In scene perception systems, object detection is a critical step. This involves capturing images of the scene using a sensing device. These images are then fed into a target detection model, which then outputs the corresponding detection results, enabling scene perception and classification. Typically, the image acquisition quality of the sensing device significantly impacts the effectiveness of target detection in the scene. For example, different sensing device mounting heights and viewing angles can cause varying degrees of deformation or scaling of objects in the captured image. For outdoor applications, image quality is easily affected by weather conditions such as rain, fog, and overcast skies. These factors can significantly reduce target detection accuracy.

[0005] Summary of the Invention

[0006] Embodiments of the present application provide a target detection method, an electronic device, and a computer-readable storage medium.

[0007] In a first aspect, an embodiment of the present application provides a target detection method, which includes: collecting an original image and obtaining image parameters of the original image; when the image parameters of the original image exceed the corresponding reference parameter range, performing image enhancement processing on the original image to obtain an enhanced image; and inputting the enhanced image into a pre-trained target detection model to obtain a target detection result.

[0008] In a second aspect, an embodiment of the present application provides a target detection device, which includes: an image acquisition module, which is configured to acquire an original image and obtain image parameters of the original image; an image enhancement module, which is configured to perform image enhancement processing on the original image to obtain an enhanced image when the image parameters of the original image exceed the corresponding reference parameter range; and an image detection module, which is configured to input the enhanced image into a pre-trained target detection model to obtain a target detection result.

[0009] In a third aspect, an embodiment of the present application provides an electronic device comprising: one or more processors; and a memory storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the target detection method as described in the first aspect above.

[0010] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the target detection method as described in the first aspect above. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] FIG1 is a schematic diagram of the steps of a target detection method provided in an embodiment of the present application;

[0012] FIG2 is a schematic diagram of steps of an image enhancement process provided by an embodiment of the present application;

[0013] FIG3 is a schematic diagram of steps for determining a reference parameter range provided in an embodiment of the present application;

[0014] FIG4 is a schematic diagram of sub-steps of step S250 in FIG3 ;

[0015] FIG5 is a schematic diagram of steps for obtaining image parameters provided by an embodiment of the present application;

[0016] FIG6 is a schematic diagram of the training steps of a target detection model provided in an embodiment of the present application;

[0017] FIG7 is a flow chart of a target detection method provided in an embodiment of the present application;

[0018] FIG8a is a schematic diagram of an application scenario of the target detection method according to an embodiment of the present application;

[0019] FIG8 b is a schematic diagram of another application scenario of the target detection method according to an embodiment of the present application;

[0020] FIG9 is a schematic structural diagram of a target detection device provided in an embodiment of the present application;

[0021] FIG10 is a schematic structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the technical solution of the present application, the target detection method, electronic device and computer-readable storage medium provided in the present application are described in detail below with reference to the accompanying drawings.

[0023] Hereinafter, example embodiments will be described more fully in conjunction with the accompanying drawings, but the described example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these examples are provided so that this application will be thorough and complete and will fully enable those skilled in the art to understand the scope of this application.

[0024] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0025] The terms used herein are used only to describe specific embodiments and are not intended to limit this application. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of" are used in this specification, they specify the presence of features, wholes, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or groups thereof.

[0026] In the description of the embodiments of the present application, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.

[0027] In the description of the embodiments of this application, any embodiment or design scheme using "exemplary," "for example," or "for example" should not be understood as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0028] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly identifying the technical features being referred to. Thus, features specified as "first" or "second" may explicitly or implicitly include one or more of such features. The terms "include," "comprising," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0029] In order to facilitate understanding of the solutions of the embodiments of the present application and to provide a clear and concise description of the following embodiments, a brief introduction to the relevant technologies is first given.

[0030] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0031] Scene perception refers to the use of sensory devices to perceive dynamic information about the surrounding environment, analyze and judge the user's current environment, and thus provide more intelligent and personalized services. Object detection is a key step in scene perception. It typically involves processing scene images captured by the sensory device using a target detection model, outputting corresponding target detection results, and then performing scene perception and classification based on these results. The image acquisition performance of the sensory device typically significantly impacts the effectiveness of target detection in the scene. For example, different sensor device mounting heights and viewing angles can cause varying degrees of deformation or scaling of objects in the captured image. For outdoor applications, image quality is easily affected by weather conditions such as rain, fog, and overcast skies. These factors can significantly reduce target detection accuracy.

[0032] Scene perception systems can be applied to vehicles' Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS), as well as to various driving functions (such as Adaptive Cruise Control (ACC) and Automatic Emergency Braking (AEB), etc. They can also be applied to scenarios such as Device to Device Communication (D2D), Vehicle to Everything (V2X), Vehicle to Vehicle (V2V), Long Term Evolution-Vehicle (LTE-V), and Long Term Evolution-Machine (LTE-M).

[0033] In a possible application example of vehicle traffic scene perception, an image acquisition device is used to capture the surrounding environment of the vehicle to generate an image, and the image is transmitted to a target detection device. The target detection device then performs target detection based on the image captured by the image acquisition device. The target may include but is not limited to parking spaces, people, obstacles, lane lines, construction areas, buildings, signs, trees, etc. Based on the target detection results, the surrounding environment of the vehicle is understood, scene perception is achieved, and the vehicle control system makes corresponding decisions. Among them, the target detection device can be installed on the vehicle terminal or on a server that is communicatively connected to the vehicle terminal. The target detection device is deployed with a trained target detection model, which can be but is not limited to an R-CNN model, a Fast R-CNN model, a Faster R-CNN model, a Mask R-CNN model, a YOLO model, an SSD model, etc.

[0034] The image acquisition device may include one or more cameras (i.e., perception devices) mounted on the vehicle body, such as surround-view cameras, monocular cameras, binocular cameras, and so on. Due to differences in camera mounting angles or viewing angles, the images captured by different cameras may have varying image quality. For example, when a vehicle is traveling at high speeds, images captured by individual cameras may be distorted. This can lead to differences in the accuracy of target detection performed by the target detection device on images captured by different cameras.

[0035] When the vehicle is in different outdoor environments, the quality of the images collected by the camera will also deviate. For example, in rainy or cloudy environments, the images collected by the camera may be less bright than those collected on sunny days, which will affect the accuracy of target detection. For example, when the vehicle is driving in a tunnel with insufficient light, the images collected by the camera may also have insufficient brightness. The above situation will also cause the target detection device to have different accuracy when performing target detection on images collected in different environments.

[0036] In addition, the data sets used for training target detection models are usually images collected under normal conditions. Once the target detection model changes its deployment equipment or the image acquisition device, the model needs to be retrained and adjusted, otherwise it will not be able to adapt well to the changing target forms in the scene.

[0037] In order to solve the problem that the target detection device has different detection accuracy when facing images collected by different sensing devices or different environments, the embodiments of the present application provide a target detection method, electronic device and computer-readable storage medium. In the embodiments of the present application, for the collected original image, parameter analysis is performed on the original image to obtain the image parameters of the original image; then the image parameters of the original image are compared with the corresponding reference parameter range to determine whether the original image meets the detection conditions of the target detection model according to the comparison result; in the embodiment of the present application, when the image parameters of the original image exceed the corresponding reference parameter range, it is considered that the original image does not meet the detection conditions of the target detection model, and the original image is subjected to image enhancement processing to obtain an enhanced image that meets the detection conditions of the target detection model; and then the enhanced image is input into a pre-trained target detection model to obtain a target detection result. Through the solution of the embodiments of the present application, the applicability of the target detection model can be improved, so that the target detection model can obtain good detection effects when facing images collected by different sensing devices or different environments.

[0038] The target detection method of the embodiment of the present application may be executed by a terminal device. The terminal device may be an intelligent roadside device, such as a road side edge computing (REC) device, a road side unit (RSU), or a roadside sensing device in a smart park. The terminal device may also be an intelligent vehicle-mounted device, a smart phone, a game console, a desktop computer, a tablet computer, a laptop computer, a smart TV, an intelligent voice interaction device, a smart home appliance, or other terminal devices such as intelligent transportation equipment, smart home equipment, a robot, and a drone.

[0039] The execution subject of the target detection method of the embodiment of the present application can be a server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. This embodiment of the present application is not limited to this.

[0040] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0041] It should be noted that in each specific embodiment of the present application, when it comes to the need to perform relevant processing based on the attribute information or attribute information set of an object (such as a user, etc.) and other data related to the characteristics of the object, the permission or consent of the corresponding object will be obtained first, and the collection, use and processing of such data will comply with relevant laws, regulations and standards. In addition, when the embodiment of the present application needs to obtain the attribute information of an object, a separate permission or separate consent of the corresponding object will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the separate permission or separate consent of the corresponding object, the relevant data of the object necessary to enable the normal operation of the embodiment of the present application will be obtained.

[0042] The target detection method, target detection device, electronic device, and storage medium provided in the embodiments of the present application are illustrated by the following embodiments. First, the target detection method in the embodiments of the present application is described.

[0043] Please refer to Figure 1, which is a schematic diagram of the steps of a target detection method provided by an embodiment of the present application. As shown in Figure 1, the target detection method includes the following steps S110-S130, and each step is introduced in sequence below:

[0044] Step S110: capturing an original image and obtaining image parameters of the original image.

[0045] The raw image can be captured by a sensing device, which can be a camera, a radar such as a millimeter-wave radar or lidar, or another sensor such as a CMOS or CCD image sensor. The object's optical image is projected through a lens onto a photosensitive element, which can be a charge-coupled device (CCD) or complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, which is then transmitted to the image signal processor (ISP) for conversion into a digital image signal. The ISP outputs the digital image signal to the DSP for processing, which converts the digital image signal into an image signal in a standard RGB (also known as the Blue-Green-Red (BGR) color space) or YUV (where Y represents brightness, U represents hue, and V represents saturation) format.

[0046] In an embodiment of the present application, the original image may be a static image captured by a camera, or a video frame in a dynamic video captured by a camera.

[0047] In the embodiment of the present application, the image parameters may include one or more of the following parameters: an image size parameter, an image deformation degree parameter, an image blur degree parameter, or an image brightness parameter.

[0048] Regarding the image size parameter, it affects the size of the target object in the image. This is caused by the different distances between the sensor assembly position and the target object. Regarding the image deformation degree parameter, it affects the deformation of the same target in the image. This is caused by the inconsistent height of the sensor assembly position. The degree of deformation of the target object in the pictures obtained by the sensor at different heights is also different. Regarding the image blur, this is because the sensor is installed in an outdoor environment and is affected by the weather. For example, heavy rain, sandstorms, fog, etc., the line of sight of the sensor is impaired, resulting in image blur and affecting the detection effect. Regarding the brightness parameter, this influencing factor will also affect the detection effect when the pictures collected by the sensor are dark on cloudy days or at night, so it also needs to be adjusted. In addition to the above four parameters, other parameters such as contrast, exposure factor, etc., all parameters that affect image quality can be taken into consideration.

[0049] The image size parameter can be determined by detecting the pixel value of the image. For example, if the width of the image is 100 pixels and the height is 200 pixels, then the size of the image is 100x200 pixels.

[0050] The image deformation degree parameter can be obtained by inputting the original image into a pre-trained image deformation detection model. The model then performs deformation detection on the target area in the image and outputs the corresponding image deformation degree parameter. The image deformation detection model can be a typical classification model, and the image deformation degree parameter can be represented by a deformation degree label. For example, a deformation degree label of 0 output by the image deformation detection model indicates that the image is not deformed; a deformation degree label of 1 output by the image deformation detection model indicates that the image is deformed.

[0051] The image blur parameter can be obtained by inputting the original image into a pre-trained image blur detection model, which then performs blur detection on the target area in the image and outputs the corresponding image blur parameter. The image blur detection model can be a typical classification model, and the image blur parameter can be represented by a blur label. For example, a blur label of 0 output by the image blur detection model indicates that the image is not blurred, while a blur label of 1 output by the image blur detection model indicates that the image is blurred.

[0052] The image brightness parameter can be determined by detecting the brightness value of the pixels in the target area of ​​the image. The unit of the image brightness parameter can be candela per square meter (cd / m2) or nits.

[0053] In an embodiment of the present application, after the original image is collected, parameter analysis is performed on the original image to determine the image parameters of the original image, so as to determine whether the quality of the original image meets the detection conditions for target detection based on the image parameters of the original image.

[0054] Step S120 : When the image parameters of the original image exceed the corresponding reference parameter range, image enhancement processing is performed on the original image to obtain an enhanced image.

[0055] In an embodiment of the present application, the reference parameter range can be determined based on the image parameters of the successful verification image set, wherein the successful verification image set includes multiple successful verification images, and the successful verification images represent images that are successfully detected by the target detection model.

[0056] For example, a sample image is manually labeled as a road construction warning sign. This sample image is input into the target detection model, and the target detection model outputs the result of a road construction warning sign. That is, if the target detection model output is consistent with the manually labeled information, the sample image is considered to have been successfully detected by the target detection model, and the sample image is classified as a successfully verified image. A successfully verified image is generally considered to have image quality that meets the target detection conditions in order to be successfully detected by the target detection model. Therefore, the embodiments of the present application collect successfully verified images and perform parameter statistical analysis on the images of the successfully verified images to obtain a baseline parameter range that meets the target detection conditions.

[0057] In the embodiment of the present application, the image parameters may include parameters of multiple parameter categories, and the parameters of each parameter category correspond to a reference parameter range. That is, the image parameters of an image include parameters of multiple parameter categories, and there are also multiple reference parameter ranges, and the multiple reference parameter ranges correspond to the parameters of the multiple parameter categories one by one. For example, the image parameters of the original image are represented by the set {A1, A2, ..., A n} means that the set {A1, A2, ..., A n} includes parameters of n parameter categories, each parameter category corresponds to a reference parameter range, and the reference parameter ranges corresponding to all parameter categories are available in the set {B1, B2, ..., B n}express.

[0058] Please refer to Figure 2, which is a schematic diagram of the steps of an image enhancement process provided by an embodiment of the present application. As shown in Figure 2, when the image parameters of the original image exceed the corresponding baseline parameter range, the original image is subjected to image enhancement processing to obtain an enhanced image. In some embodiments, the method may include the following steps S121 and S122: Step S121, comparing the image parameters of each parameter category of the original image with the corresponding baseline parameter range to determine the parameter category of the original image that exceeds the baseline parameter range; Step S122, performing corresponding image enhancement processing on the original image according to the parameter category that exceeds the baseline parameter range to obtain an enhanced image.

[0059] In one possible exemplary embodiment, image parameters corresponding to the original image are obtained through parameter analysis. The image parameters corresponding to the original image include parameters from multiple parameter categories: image size parameter A1, image deformation parameter A2, image blur parameter A3, and image brightness parameter A4. A baseline parameter range B1 corresponding to image size parameter A1, a baseline parameter range B2 corresponding to image deformation parameter A2, a baseline parameter range B3 corresponding to image blur parameter A3, and a baseline parameter range B4 corresponding to image brightness parameter A4 are obtained, i.e., the baseline parameter ranges corresponding to each parameter category are obtained. The parameters of each parameter category are then compared with the corresponding baseline parameter range to determine whether at least one parameter category exceeds the corresponding baseline parameter range. If at least one parameter category exceeds the corresponding baseline parameter range, image enhancement processing is performed on the original image based on the parameter category that exceeds the baseline parameter range, so that the enhanced original image meets the baseline parameter range.

[0060] In a possible exemplary embodiment, performing corresponding image enhancement processing on the original image according to the parameter category that exceeds the baseline parameter range may include at least one of the following steps: when the parameter category that exceeds the baseline parameter range is the image size parameter, performing scaling adjustment processing on the original image to adjust the image size parameter of the original image to the corresponding baseline parameter range through scaling adjustment processing; or when the parameter category that exceeds the baseline parameter range is the image deformation degree parameter, performing scale adjustment processing on the original image to adjust the image deformation parameter of the original image to the corresponding baseline parameter range through scale adjustment processing; or when the parameter category that exceeds the baseline parameter range is the image blur degree parameter, performing image deblurring processing on the original image to adjust the image blur degree parameter of the original image to the corresponding baseline parameter range through deblurring processing; or when the parameter category that exceeds the baseline parameter range is the image brightness parameter, performing image shading processing on the original image to adjust the image brightness parameter of the original image to the corresponding baseline parameter range through image shading processing.

[0061] It is understood that if the image size parameters of the original image exceed the corresponding reference parameter range, the original image can be scaled to adjust the image size. Image scaling includes two types of processing: reducing the image and enlarging the image. Reducing the image can be achieved through downsampling or downsampling, and enlarging the image can be achieved through upsampling or image interpolation.

[0062] It is understood that if the image blur parameter of the original image exceeds the corresponding reference parameter range, the original image may be rescaled to reduce the degree of image distortion. Rescaling may include adjusting the ratio of the width and height of the target area in the image, such as by stretching or compressing the image, to eliminate image distortion.

[0063] It is understood that if the image deformation degree parameter of the original image exceeds the corresponding baseline parameter range, the original image can be deblurred to improve image clarity. Deblurring can be done by interpolating a low-resolution image, then detecting edges in the high-resolution image, and then performing special processing on the edges and nearby pixels to remove blur and enhance the image edges.

[0064] It is understood that if the image brightness parameter of the original image exceeds the corresponding reference parameter range, then the original image can be subjected to image shading processing to bring the image brightness within the reference parameter range. Image shading processing includes image brightness enhancement processing or image brightness reduction processing, which can be implemented using an image contrast and brightness adjustment algorithm, such as the gamma transformation algorithm in the spatial domain method.

[0065] In an embodiment of the present application, at least one image parameter of the original image is obtained, and it is determined whether each image parameter exceeds the corresponding baseline parameter range. If any image parameter exceeds the corresponding baseline parameter range, corresponding image enhancement processing is performed so that each image parameter of the image is within the corresponding baseline parameter range, and the image finally obtained by the enhancement processing is used as the enhanced image.

[0066] Step S130: input the enhanced image into a pre-trained target detection model to obtain a target detection result.

[0067] In an embodiment of the present application, if the image parameters of the original image exceed the reference parameter range, the original image is first enhanced to obtain an enhanced image, and then the enhanced image is input into the target detection model to obtain the corresponding target detection result.

[0068] It can be understood that, for the original image, if the image parameters of each parameter category do not exceed the corresponding benchmark parameter range, then the original image can be directly input into the target detection model to obtain the corresponding target detection result.

[0069] Please refer to Figure 3, which is a schematic diagram of the steps for determining the range of the benchmark parameters provided by an embodiment of the present application. As shown in Figure 3, the range of the benchmark parameters involved in the embodiment of the present application can be determined by the following steps S210-S250: Step S210, obtaining a verification data set, wherein the verification data set includes multiple verification sample images and annotation information corresponding to each verification sample image; Step S220, inputting each verification sample image into the target detection model respectively to obtain the target detection results corresponding to each verification sample image; Step S230, determining multiple successful verification images from multiple verification sample images based on the annotation information and target detection results corresponding to each verification sample image; Step S240, obtaining the image parameters corresponding to all successful verification images; Step S250, performing statistical analysis on the image parameters corresponding to all successful verification images to obtain the range of the benchmark parameters.

[0070] In an embodiment of the present application, each verification sample image is input into a trained target detection model, and the target detection result corresponding to each verification sample image is output by the target detection model. Then, for each verification sample image, the annotation information corresponding to the verification sample image and the target detection result are compared. If the corresponding annotation information and the target detection result match, it is considered that the verification sample image is successfully detected by the target detection model, and then the verification sample image is used as a successful verification image. Collect multiple successful verification images, perform parameter analysis on all successful verification images, obtain image parameters corresponding to each successful verification image, perform statistical analysis on the image parameters corresponding to all successful verification images, and obtain a reference parameter range. The reference parameter range obtained in this way can reflect the image quality requirements of the target detection model for the input image. Therefore, in the implementation of this application, the reference parameter range obtained based on the successful verification image is used as the detection condition parameter of the target detection model. The original image is determined based on the image parameters of the original image to determine whether it meets the detection condition parameters of the target detection model. If not, the original image is subjected to image enhancement processing until the image parameters meet the reference parameter range.

[0071] A validation dataset can be a set of sample images for a specific scenario, such as a V2X scenario, containing hundreds or thousands of sample images. Each validation sample image is labeled according to the target category to be detected (e.g., vehicle, pedestrian, obstacle, etc.), obtaining the corresponding annotation information for the validation sample image.

[0072] The target detection result may include a target classification result, as shown in FIG4 , which is a schematic diagram of the sub-steps of step S250 in FIG3 . As shown in FIG4 , performing statistical analysis on the image parameters corresponding to all successfully verified images to obtain a reference parameter range may include the following steps S251-S253: Step S251 , classifying the successfully verified images according to the target classification results corresponding to each successfully verified image to obtain successfully verified images of multiple classification categories; Step S252 , for each classification category, calculating the mean of the image parameters corresponding to all successfully verified images belonging to the same classification category to obtain the mean of the image parameters of the corresponding classification category; Step S253 , performing statistical analysis on the mean of the image parameters of all classification categories to obtain the reference parameter range.

[0073] It is understood that the successfully verified image is input into the target detection model, and the target detection result output by the target detection model is the target classification result. For each successfully verified image, based on the target classification result of the successfully verified image, the successfully verified image is added to the corresponding classification directory. The image parameters of all successfully verified images in the same classification directory are obtained, and the mean image parameters of all successfully verified images are calculated. The mean image parameters of all classification categories are statistically analyzed, including but not limited to variance, standard deviation, mean square error, and mean calculation, to obtain the baseline parameter range.

[0074] The following is an example of calculating the reference parameter range corresponding to the image size parameter. In one possible embodiment, according to the target classification result, the successfully verified image is divided into multiple classification categories of successfully verified images, for example, divided into classification categories such as vehicles, pedestrians and obstacles. According to the classification category, the mean image size parameter of the successfully verified images of each classification category is calculated, for example, the mean image size parameter of the successfully verified images of the vehicle category, the mean image size parameter of the successfully verified images of the pedestrian category, and so on. Then, the image size parameter means of all classification categories are statistically analyzed to obtain the reference parameter range corresponding to the image size parameter. By analogy, the reference parameter range corresponding to the image deformation degree parameter, the image blur degree parameter, and the image brightness parameter can be calculated in a similar manner.

[0075] In one possible exemplary embodiment, image parameters include static parameters and dynamic parameters. Static parameters may include at least one of an image size parameter and an image deformation parameter, while dynamic parameters may include at least one of an image blur parameter and an image brightness parameter. For images captured by the same sensing device, the static parameters of each frame generally do not differ significantly, while the dynamic parameters of each frame may vary significantly. Therefore, for images captured by the same sensing device, only the static parameters of the first frame can be calculated and applied to subsequent images; however, the dynamic parameters need to be calculated for each frame.

[0076] Please refer to Figure 5, which is a schematic diagram of the steps for obtaining image parameters provided by an embodiment of the present application. As shown in Figure 5, capturing an original image and obtaining image parameters of the original image may include the following steps S111-S112: Step S111, obtaining the first frame of the original image captured by the sensing device, determining corresponding static parameters based on the first frame of the original image, and using the static parameters corresponding to the first frame of the original image as the static parameters corresponding to each frame of the original image; Step S112, obtaining the original image captured by the sensing device at a preset time interval, and performing dynamic parameter analysis on each frame of the captured original image to obtain the dynamic parameters corresponding to the original image.

[0077] For example, the sensing device is turned on, and the first frame image captured by the sensing device is obtained. The static parameters and dynamic parameters of the first frame image are calculated. If the image size parameter of the first frame image (a static parameter) exceeds the corresponding reference parameter range, the first frame image is scaled and adjusted. The second frame image captured by the sensing device is then obtained, and the static parameters of the first frame image are applied to the second frame image. The static parameters are no longer calculated repeatedly, and only the dynamic parameters of the second frame image are calculated. If the image brightness parameter of the second frame image (a dynamic parameter) exceeds the corresponding reference parameter range, the second frame image is subjected to image shading processing. In addition, the second frame image continues to use the image size parameter of the previous first frame image, that is, the image size parameter of the second frame image also exceeds the corresponding reference parameter range, and the second frame image needs to be scaled and adjusted.

[0078] It can be understood that since the static parameters of each frame of image captured by the same sensing device generally do not differ too much, this embodiment only calculates the static parameters of the first frame of image, and uses the calculated static parameters of the first frame of image in the subsequently captured images to improve processing efficiency; and there may be large differences between the dynamic parameters of each frame of image, so the dynamic parameters are calculated for each frame of image to ensure the accuracy of quality judgment of each frame of image.

[0079] Please refer to Figure 6, which is a schematic diagram of the training steps of a target detection model provided by an embodiment of the present application. As shown in Figure 6, the target detection model can be trained by the following steps: Step S310, obtaining a training data set, wherein the training data set includes multiple training sample images and annotation information corresponding to each training sample image, and the annotation information corresponding to the training sample image includes target position annotation information and classification annotation information; Step S320, inputting multiple training sample images into the initial neural network model respectively to obtain target detection results corresponding to each training sample image, and the target detection results include target position detection results and target classification results; Step S330, determining the loss value according to the target position detection results, target position annotation information, target classification results and classification annotation information corresponding to each training sample image; Step S340, when the loss value meets the preset training end condition, the current neural network model is used as the target detection model; Step S350, when it is determined that the training end condition is not met according to the loss value, the model parameters of the neural network model are adjusted, and the neural network model with adjusted model parameters is continued to be trained based on the training data set.

[0080] It can be understood that the annotation information corresponding to the training sample image includes target position annotation information and classification annotation information, and the target detection result output by the neural network model for the training sample image includes target position detection result and target classification result. A first loss value can be calculated based on the target position annotation information and the target position detection result, and a second loss value can be calculated based on the classification annotation information and the target classification result. The total loss value can be determined based on the first loss value and the second loss value. The total loss value is used to determine whether the current neural network model meets the training end condition. If it meets the condition, the current neural network model is used as the target detection model. Otherwise, the model parameters of the neural network model are adjusted, and the neural network model with the adjusted model parameters is continued to be trained based on the training data set. In this embodiment, the target position annotation information and the classification annotation information are used as training targets so that the model can accurately detect the position of the target object, that is, locate the target object, and then accurately detect the classification corresponding to the target object based on the located target object, thereby improving the detection accuracy of the target detection model.

[0081] In an embodiment of the present application, AI image enhancement technology is integrated with scene perception technology, and AI image enhancement technology is used to preprocess the sequence images collected by the perception unit to make them meet the requirements of the target detection model and improve the target detection recognition rate.

[0082] In an embodiment of the present application, the determination of the benchmark parameters includes: training a target detection model, using the model to perform target detection and identification on the scene verification data set collected by the perception device one by one, and extracting and saving pictures that can successfully detect the target according to the classification directory according to the target classification actually required to be detected (such as cars, pedestrians, obstacles, etc.); then calculating the parameters of all successfully detected pictures (including but not limited to four parameters: proportion, degree of deformation, degree of blur, and brightness), and then calculating the mean according to the classification, and finally performing statistical analysis and calculation on the parameters of all target classifications to obtain the optimal picture parameter range that meets the model. The parameter range calculated here is the parameter benchmark range in this method.

[0083] In an embodiment of the present application, the judgment of whether the collected image needs parameter adjustment and the image parameter adjustment process include: the computing device perceives the scale, deformation degree, blur degree, and brightness parameter of the image collected by the sensor (this is not limited to these four parameters, and other parameters that may affect the image quality may be added or deleted), and compares them with the four parameter reference ranges respectively. If the parameters are all within the reference error range, they are considered qualified images and directly output to the next step for target detection and identification; if the parameter difference exceeds the reference error range, they are considered unqualified images, and adaptive AI image enhancement processing is performed on them so that their various parameters are within the allowable error range before they can be output to the next step for detection. For example, first determine which parameter is out of range and then input the image into the corresponding image processing module. If the blur degree is unqualified, image deblurring is performed. If the image size is out of range, image scaling is performed. If the image brightness is out of range, image shading is performed. If the image deformation degree is out of range, image scale adjustment is performed. If there are other parameters that need to be considered, they can also be customized and added to the module for judgment analysis and adaptive adjustment until all parameters meet the requirements, otherwise the processing process is looped.

[0084] In related technologies, the method of improving the universality of target detection model equipment mainly focuses on multi-sensor fusion perception. Once the deployment equipment is replaced, the model needs to be retrained and adjusted, otherwise it cannot adapt well to the changing target forms in the scene. From the perspective of image processing, the embodiment of the present application adopts an AI image enhancement method that can adaptively pre-process images such as target scale, deformation, blur, too dark and other problems, so that it can adapt to the assembly height and viewing angle of various scene perception devices, adaptively solve various problems of poor target detection caused by image quality problems, and achieve stable target detection effect.

[0085] AI image enhancement methods in related technologies all perform specific image enhancements, such as dehazing, magnification, and contrast enhancement. This method employs a classification enhancement mechanism that simultaneously trains the target detection model on the range of image parameters that the model can successfully detect. Through parameter analysis, different image enhancement strategies are then employed to adaptively enhance images with various problems, ensuring they meet the image requirements of the target detection model.

[0086] Please refer to Figure 7, which is a flow chart of a target detection method provided by an embodiment of the present application. As shown in Figure 7, the target detection method includes the following steps: obtaining a training data set, wherein the training data set includes multiple training sample images and annotation information corresponding to each training sample image, and the annotation information corresponding to the training sample image includes target position annotation information and classification annotation information; inputting multiple training sample images into the initial neural network model respectively to obtain target detection results corresponding to each training sample image, and the target detection results include target position detection results and target classification results; determining a loss value according to the target position detection results, target position annotation information, target classification results and classification annotation information corresponding to each training sample image; training the neural network model according to the loss value to obtain a trained target detection model; obtaining a verification data set, wherein the verification data set includes multiple verification sample images and annotation information corresponding to each verification sample image; inputting each verification sample image into the target detection model respectively to obtain each a target detection result corresponding to a verification sample image; according to the annotation information and target detection results corresponding to each verification sample image, multiple verification success images are determined from the multiple verification sample images; parameter analysis is performed on all the verification success images respectively to obtain the image parameters corresponding to each verification success image; statistical analysis is performed on the image parameters corresponding to all the verification success images to obtain the benchmark parameter range; the original image captured by the perception device is obtained, and the image parameters of the original image are obtained; if the image size parameter exceeds the corresponding benchmark parameter range, the original image is scaled and adjusted; if the image deformation degree parameter exceeds the corresponding benchmark parameter range, the original image is scaled and adjusted; if the image blur degree parameter exceeds the corresponding benchmark parameter range, the original image is deblurred; if the image brightness parameter exceeds the corresponding benchmark parameter range, the original image is subjected to image shading processing; an enhanced image is obtained based on the enhanced original image; the enhanced image is input into a pre-trained target detection model to obtain a target detection result.

[0087] Please refer to Figure 8a, which is a schematic diagram of an application scenario of the target detection method of an embodiment of the present application. The scenario shown in Figure 8a includes an intelligent roadside device and multiple roadside sensing devices. The intelligent roadside device can be installed on a roadside pole and communicate with multiple roadside sensing devices. The roadside sensing device can be a camera, radar or other device. The intelligent roadside device receives the original image collected by each roadside sensing device, and performs the following steps for the original image collected by each camera: obtaining the image parameters of the original image, and then comparing the image parameters of the original image with the corresponding reference parameter range to determine whether the original image meets the detection conditions of the target detection model based on the comparison result; when the image parameters of the original image exceed the corresponding reference parameter range, it is considered that the original image does not meet the detection conditions of the target detection model, and the original image is subjected to image enhancement processing to obtain an enhanced image that meets the detection conditions of the target detection model; and then the enhanced image is input into the pre-trained target detection model to obtain the target detection result.

[0088] Please refer to Figure 8b, which is a schematic diagram of another application scenario of the target detection method of an embodiment of the present application. The scenario shown in Figure 8b includes a vehicle, and the vehicle includes an on-board terminal and multiple sensing devices, and the multiple sensing devices include an interior view camera, a rear view camera, a front camera, a side view camera, and a surround view camera, and each camera is connected to the on-board terminal respectively. The on-board terminal receives the original image captured by each camera, and for the original image captured by each camera, performs the following steps: obtain the image parameters of the original image, and then compare the image parameters of the original image with the corresponding reference parameter range to determine whether the original image meets the detection conditions of the target detection model according to the comparison result; when the image parameters of the original image exceed the corresponding reference parameter range, it is considered that the original image does not meet the detection conditions of the target detection model, and the original image is subjected to image enhancement processing to obtain an enhanced image that meets the detection conditions of the target detection model; and then the enhanced image is input into the pre-trained target detection model to obtain the target detection result.

[0089] It should be understood that although operations are described in a particular order in the drawings in the embodiments of the present application, this should not be construed as requiring that these operations be performed in the particular order shown or in a serial order, or that all of the illustrated operations be performed to obtain the desired results. In certain circumstances, multitasking and parallel processing may be advantageous.

[0090] Please refer to Figure 9, which is a schematic diagram of the structure of a target detection device provided in an embodiment of the present application. As shown in Figure 9, the target detection device includes: an image acquisition module configured to acquire an original image; an image parameter acquisition module configured to obtain image parameters of the original image; an image enhancement module configured to perform image enhancement processing on the original image to obtain an enhanced image when the image parameters of the original image exceed the corresponding reference parameter range; and an image detection module configured to input the enhanced image into a pre-trained target detection model to obtain a target detection result.

[0091] In one embodiment, the target detection device also includes a benchmark parameter determination module, which includes: a verification data acquisition unit, which is configured to acquire a verification data set, input each verification sample image into the target detection model respectively, and obtain the target detection result corresponding to each verification sample image, wherein the verification data set includes multiple verification sample images and annotation information corresponding to each verification sample image; a verification success image determination unit, which is configured to determine multiple verification success images from multiple verification sample images based on the annotation information and target detection results corresponding to each verification sample image, and perform parameter analysis on all the verification success images respectively to obtain the image parameters corresponding to each verification success image; a benchmark parameter determination unit, which is configured to perform statistical analysis on the image parameters corresponding to all the verification success images to obtain the benchmark parameter range.

[0092] In one embodiment, the target detection result includes a target classification result; the benchmark parameter determination unit is configured to: classify according to the target classification results corresponding to each successfully verified image to obtain successfully verified images of multiple classification categories; for each classification category, calculate the mean of the image parameters corresponding to all successfully verified images belonging to the same classification category to obtain the mean of the image parameters of the corresponding classification category; perform statistical analysis based on the mean of the image parameters of all classification categories to obtain the benchmark parameter range.

[0093] In one embodiment, the image parameters include parameters of multiple parameter categories, and each parameter category corresponds to a reference parameter range.

[0094] In one embodiment, the image parameters include one or more of the following parameters: an image size parameter, an image deformation parameter, an image blur parameter, or an image brightness parameter.

[0095] In one embodiment, the image enhancement module includes: a comparison unit, which is configured to compare the image parameters of each parameter category of the original image with the corresponding benchmark parameter range to determine the parameter category of the original image that exceeds the benchmark parameter range; an enhancement processing unit, which is configured to perform corresponding image enhancement processing on the original image according to the parameter category that exceeds the benchmark parameter range to obtain an enhanced image.

[0096] In one embodiment, the enhancement processing unit includes at least one of the following: a scaling adjustment unit, configured to perform scaling adjustment processing on the original image when the parameter category exceeding the baseline parameter range is an image size parameter, so as to adjust the image size parameter of the original image to the corresponding baseline parameter range through the scaling adjustment processing; or a scale adjustment unit, configured to perform scale adjustment processing on the original image when the parameter category exceeding the baseline parameter range is an image deformation degree parameter, so as to adjust the image deformation parameter of the original image to the corresponding baseline parameter range through the scale adjustment processing; or a deblurring processing unit, configured to perform image deblurring processing on the original image when the parameter category exceeding the baseline parameter range is an image blur degree parameter, so as to adjust the image blur degree parameter of the original image to the corresponding baseline parameter range through the deblurring processing; or a shading processing unit, configured to perform image shading processing on the original image when the parameter category exceeding the baseline parameter range is an image brightness parameter, so as to adjust the image brightness parameter of the original image to the corresponding baseline parameter range through the image shading processing.

[0097] In one embodiment, the image parameters include static parameters and dynamic parameters; the image parameter acquisition module is configured to: acquire a first frame of original image captured by a sensing device, determine corresponding static parameters based on the first frame of original image, and use the static parameters corresponding to the first frame of original image as static parameters corresponding to each frame of original image; acquire the original image captured by the sensing device at a preset time interval, and perform dynamic parameter analysis on the original image for each frame of original image captured to obtain dynamic parameters corresponding to the original image; wherein the static parameters include at least one of an image size parameter and an image deformation degree parameter, and the dynamic parameters include at least one of an image blur degree parameter and an image brightness parameter.

[0098] In one embodiment, the target detection device also includes a training module, which is configured to: obtain a training data set, wherein the training data set includes multiple training sample images and annotation information corresponding to each training sample image, and the annotation information corresponding to the training sample image includes target position annotation information and classification annotation information; input the multiple training sample images into the initial neural network model respectively to obtain target detection results corresponding to each training sample image, and the target detection results include target position detection results and target classification results; determine a loss value based on the target position detection results, target position annotation information, target classification results and classification annotation information corresponding to each training sample image; when the loss value meets the preset training end condition, use the current neural network model as the target detection model; when it is determined according to the loss value that the training end condition is not met, adjust the model parameters of the neural network model, and continue to train the neural network model with adjusted model parameters based on the training data set.

[0099] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0100] Please refer to Figure 10, which is an electronic device provided in an embodiment of the present application. The electronic device 900 includes but is not limited to: at least one processor 910; at least one memory 920, which is configured to store at least one program; when the at least one program is executed by the at least one processor 910, the target detection method described in any of the above embodiments is executed.

[0101] It should be understood that the processor 910 and the memory 920 may be connected via a bus or other means.

[0102] It should be understood that the processor 910 can adopt a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. Alternatively, the processor 910 adopts one or more integrated circuits and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0103] Memory 920, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs, such as the target detection method described in any embodiment of this application. Processor 910 implements the target detection method described above by executing the non-transitory software program and instructions stored in memory 920.

[0104] The memory 920 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data for executing the target detection method described above. In addition, the memory 920 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 920 includes a memory remotely located relative to the processor 910, and these remote memories may be connected to the processor 910 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0105] The non-transient software programs and instructions required to implement the above-mentioned target detection method are stored in the memory 920. When executed by one or more processors 910, the target detection method provided by any embodiment of the present application is executed.

[0106] An embodiment of the present application further provides a computer-readable storage medium, which stores a program executable by a processor. When the program executable by the processor is executed by the processor, it is used to implement the target detection method described in any of the above embodiments.

[0107] In an embodiment of the present application, for the collected original image, parameter analysis is performed on the original image to obtain the image parameters of the original image; then the image parameters of the original image are compared with the corresponding reference parameter range to determine whether the original image meets the detection conditions of the target detection model based on the comparison result; in an embodiment of the present application, when the image parameters of the original image exceed the corresponding reference parameter range, it is considered that the original image does not meet the detection conditions of the target detection model, and the original image is subjected to image enhancement processing to obtain an enhanced image that meets the detection conditions of the target detection model; and then the enhanced image is input into a pre-trained target detection model to obtain a target detection result. Through the solution of the embodiment of the present application, the applicability of the target detection model can be improved, so that the target detection model can obtain good detection results for images collected by different sensing devices or different environments.

[0108] The computer storage medium of the embodiment of the present application can adopt any combination of one or more computer-readable media.Computer-readable media can be computer-readable signal media or computer-readable storage media.Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or components, or any combination thereof. Examples of computer-readable storage media (non-exhaustive list) include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, computer-readable storage media can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0109] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0110] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0111] Computer program code for performing the operations of the present application can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (such as through the Internet using an Internet service provider).

[0112] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the above implementation mode. Technical personnel familiar with the field can also make various equivalent modifications or substitutions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the present application.

Claims

1. A target detection method, comprising: Collecting an original image and obtaining image parameters of the original image; When the image parameter of the original image exceeds the corresponding reference parameter range, performing image enhancement processing on the original image to obtain an enhanced image; The enhanced image is input into a pre-trained target detection model to obtain a target detection result.

2. The method according to claim 1, wherein: The reference parameter range is determined by the following steps: Acquire a verification data set, wherein the verification data set includes a plurality of verification sample images and annotation information corresponding to each of the verification sample images; Input each of the verification sample images into the target detection model respectively to obtain the target detection results corresponding to each of the verification sample images; Determining a plurality of successful verification images from a plurality of the verification sample images according to the annotation information corresponding to each of the verification sample images and the target detection result; Obtaining image parameters corresponding to all the successfully verified images; Statistical analysis is performed on the image parameters corresponding to all the successfully verified images to obtain the reference parameter range.

3. The method according to claim 2, wherein: The target detection result includes a target classification result; The statistical analysis of the image parameters corresponding to all the successfully verified images to obtain the reference parameter range includes: Classify the target classification results corresponding to each of the successful verification images to obtain the successful verification images of multiple classification categories; For each of the classification categories, average calculation is performed on the image parameters corresponding to all the successfully verified images belonging to the same classification category to obtain the image parameter average corresponding to the classification category; The reference parameter range is obtained by performing statistical analysis based on the mean values ​​of the image parameters of all the classification categories.

4. The method according to any one of claims 1 to 3, wherein: The image parameters include parameters of multiple parameter categories, and each parameter of the parameter category corresponds to a reference parameter range.

5. The method according to claim 4, wherein: The image parameters include one or more of the following parameters: an image size parameter, an image deformation degree parameter, an image blur degree parameter or an image brightness parameter.

6. The method according to claim 4, wherein: When the image parameter of the original image exceeds the corresponding reference parameter range, performing image enhancement processing on the original image to obtain an enhanced image includes: Comparing the image parameters of each parameter category of the original image with the corresponding reference parameter range, and determining the parameter category of the original image that exceeds the reference parameter range; The original image is subjected to corresponding image enhancement processing according to the parameter category exceeding the reference parameter range to obtain the enhanced image.

7. The method according to claim 6, wherein: The performing corresponding image enhancement processing on the original image according to the parameter category exceeding the reference parameter range includes at least one of the following: In the case where the parameter category exceeding the reference parameter range is an image size parameter, performing a scaling adjustment process on the original image, so as to adjust the image size parameter of the original image to within the corresponding reference parameter range through the scaling adjustment process; or In the case where the parameter category exceeding the reference parameter range is an image deformation degree parameter, performing a scale adjustment process on the original image, so as to adjust the image deformation parameter of the original image to within the corresponding reference parameter range through the scale adjustment process; or In a case where the parameter category exceeding the reference parameter range is an image blur degree parameter, performing image deblurring processing on the original image, so as to adjust the image blur degree parameter of the original image to within the corresponding reference parameter range through the deblurring processing; or In the case that the parameter category exceeding the reference parameter range is an image brightness parameter, image shading processing is performed on the original image, so as to adjust the image brightness parameter of the original image to be within the corresponding reference parameter range through the image shading processing.

8. The method according to claim 1, wherein: The image parameters include static parameters and dynamic parameters; The collecting of the original image and obtaining the image parameters of the original image includes: Acquire a first frame of original image captured by a sensing device, determine the corresponding static parameters according to the first frame of original image, and use the static parameters corresponding to the first frame of original image as the static parameters corresponding to each frame of the original image; The original image collected by the sensing device is acquired at a preset time interval, and for each frame of the original image collected, dynamic parameter analysis is performed on the original image to obtain the dynamic parameters corresponding to the original image.

9. The method according to claim 8, wherein: The static parameter includes at least one of an image size parameter and an image deformation degree parameter, and the dynamic parameter includes at least one of an image blur degree parameter and an image brightness parameter.

10. The method according to claim 1, wherein: The target detection model is trained by the following steps: Acquire a training data set, wherein the training data set includes a plurality of training sample images and annotation information corresponding to each of the training sample images, and the annotation information corresponding to the training sample images includes target position annotation information and classification annotation information; Inputting the plurality of training sample images into the initial neural network model respectively, and obtaining target detection results corresponding to the respective training sample images, wherein the target detection results include target position detection results and target classification results; Determine a loss value according to the target position detection result, the target position annotation information, the target classification result and the classification annotation information corresponding to each of the training sample images; When the loss value satisfies a preset training end condition, using the current neural network model as the target detection model; When it is determined according to the loss value that the training end condition is not met, adjusting the model of the neural network model The neural network model after adjusting the model parameters is continued to be trained based on the training data set.

11. A target detection device, comprising: An image acquisition module is configured to acquire an original image and obtain image parameters of the original image; An image enhancement module is configured to perform image enhancement processing on the original image to obtain an enhanced image when the image parameter of the original image exceeds the corresponding reference parameter range; The image detection module is configured to input the enhanced image into a pre-trained target detection model to obtain a target detection result.

12. An electronic device comprising: one or more processors; A memory having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the target detection method as described in any one of claims 1-10.

13. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the target detection method according to any one of claims 1 to 10 is implemented.

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