Vehicle paint surface detection processing system based on AI

Abstract

The invention, which belongs to the technical field of vehicle paint surface detection, discloses an AI-based vehicle paint surface detection processing system comprising a platform end and a user end. The platform end comprises a paint surface detection module and a detection guidance module; the paint surface detection module is used for carrying out paint surface detection on a to-be-detected vehicle and acquiring vehicle information and a paint surface image of the to-be-detected vehicle; analyzing the vehicle information and the paint surface image to obtain a paint surface detection result; the paint surface detection result is sent to a corresponding user side; the detection guidance module is used for setting a paint surface acquisition course of a corresponding vehicle and storing the paint surface acquisition course in a course library; the user side comprises a paint surface acquisition module and a result display module; the paint surface acquisition module is used for acquiring a paint surface image of a user vehicle and sending the vehicle information and the paint surface image to the platform end; and the result display module is used for displaying the paint surface detection result, generating a vehicle model according to the vehicle information, obtaining the paint surface detection result in real time, and performing display adjustment on the vehicle model according to the paint surface detection result.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle paint surface detection, and specifically relates to an AI-based vehicle paint surface detection and processing system. Background Art

[0002] In the daily use of personal vehicles and vehicle trading scenarios, the paint surface quality directly affects the vehicle value, usage experience, and trading rights and interests. Therefore, how to detect the vehicle paint surface has become an urgent problem for personal vehicle owners. However, currently, ordinary vehicle owners often rely on non-professional channels for paint surface detection, such as roadside repair shops and non-chain beauty shops. The detection methods mainly rely on manual experience judgment, lacking a unified standard, resulting in a high error rate in the identification of defects such as scratches, color differences, and orange peel patterns, and low credibility. If personal vehicle owners need accurate detection, they need to entrust third-party institutions, such as 4S stores and professional detection companies. The single detection cost is high, and they need to make an appointment and wait, with a cumbersome process.

[0003] Based on this, in order to meet the efficient demand of personal vehicle owners for vehicle paint surface detection, the present invention provides an AI-based vehicle paint surface detection and processing system. Summary of the Invention

[0004] [[ID=1�]]To solve the problems existing in the above solutions, the present invention provides an AI-based vehicle paint surface detection and processing system.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The AI-based vehicle paint surface detection and processing system includes a platform end and a user end;

[0007] The platform end includes a paint surface detection module and a detection guidance module;

[0008] The paint surface detection module is used to perform paint surface detection on the vehicle to be inspected, obtain the vehicle information and paint surface images of the vehicle to be inspected; analyze the vehicle information and paint surface images to obtain the paint surface detection results; and send the paint surface detection results to the corresponding user end.

[0009] The detection guidance module is used to set the paint surface acquisition tutorial for the corresponding vehicle, mark the corresponding vehicle information for the paint surface acquisition tutorial, and store the paint surface acquisition tutorial in the tutorial library.

[0010] Furthermore, the setting of the paint surface acquisition tutorial includes:

[0011] Set a vehicle list, which is used to count various vehicle information; and collect the paint surface feature set of the corresponding vehicle in real time according to the vehicle list;

[0012] Determine whether the corresponding vehicles meet the tutorial usage standard according to the paint surface feature set, where the tutorial usage standard is that the paint surface images can be collected using the same paint surface collection tutorial;

[0013] Classify the vehicle information that meets the tutorial usage standard into one category to obtain the corresponding vehicle paint surface classification; the platform sets the corresponding paint surface collection tutorial for the vehicle paint surface classification.

[0014] Furthermore, when there is a new vehicle paint surface classification, identify the paint surface feature set of the corresponding vehicle, match a vehicle paint surface classification with the highest similarity according to the paint surface feature set; identify the paint surface collection tutorial of the vehicle paint surface classification, and adjust the paint surface collection tutorial according to the paint surface feature set to obtain the paint surface collection tutorial for the new vehicle paint surface classification.

[0015] The user terminal includes a paint surface collection module and a result display module;

[0016] The paint surface collection module is used to collect the paint surface image of the user's vehicle and send the vehicle information and the paint surface image to the platform terminal.

[0017] Furthermore, the method for collecting the paint surface image includes:

[0018] Obtain the vehicle information of the user's vehicle, match the corresponding paint surface collection tutorial from the tutorial library; display the paint surface collection tutorial to the user;

[0019] The user collects the image of the vehicle paint surface according to the paint surface collection tutorial to obtain the paint surface image.

[0020] Furthermore, the paint surface collection module further includes a docking collection unit, and the docking collection unit is used to dock with the image collection system of the user's vehicle, and the image collection system is used to collect images of the user's vehicle;

[0021] Obtain the vehicle collection image of the user's vehicle by the image collection system in real time, and process the vehicle collection image to obtain the vehicle paint surface image.

[0022] Furthermore, processing the collected image includes:

[0023] Locate the collected image according to the position of the user's vehicle, calibrate the quality of the collected image at the corresponding position of the user's vehicle to obtain the quality calibration result of the collected image at the corresponding position, and the quality calibration result includes qualified quality calibration and unqualified quality calibration; perform corresponding processing on the collected image according to the quality calibration result.

[0024] Furthermore, calibrating the quality of the collected image at the corresponding position of the user's vehicle includes:

[0025] Establish a quality calibration model, and the expression of the quality calibration model is:

[0026]

[0027] In the formula: s is the input data, representing the acquired image; the output data is the quality evaluation value ZP(s), and the quality evaluation value is 1 or 0.

[0028] Analyze the acquired image at the corresponding position through the quality calibration model to obtain the quality evaluation value at the corresponding position.

[0029] When the quality evaluation value is 1, the quality calibration result is that the quality calibration is qualified.

[0030] When the quality evaluation value is 0, the quality calibration result is that the quality calibration is unqualified.

[0031] Furthermore, generate a corresponding image quality model according to the quality calibration results of each position of the user's vehicle obtained in real time, and display the image quality model to the user in real time.

[0032] Furthermore, the generation of the image quality model:

[0033] Generate a visualization model of the user's vehicle according to the vehicle information, marked as the vehicle model; respectively set the display methods corresponding to qualified and unqualified quality calibrations.

[0034] Obtain the quality calibration results of the corresponding positions of the vehicle in real time, adjust the display of the paint area in the vehicle model according to the display methods corresponding to the quality calibration results to obtain the image quality model; and dynamically update the image quality model according to the quality calibration results.

[0035] The result display module is used to display the paint surface detection results, generate a vehicle model according to the vehicle information, obtain the paint surface detection results in real time, and adjust the display of the vehicle model according to the paint surface detection results; display the adjusted vehicle model to the user.

[0036] Furthermore, the vehicle model is updated in real time according to the paint surface detection results.

[0037] Furthermore, the display adjustment of the vehicle model according to the paint surface detection results includes:

[0038] Preset the display adjustment methods of the vehicle model for different paint surface detection results; identify the paint surface detection results in real time, match the corresponding display adjustment methods according to the paint surface detection results, and adjust the display of the vehicle model according to the display adjustment methods.

[0039] When the paint surface detection result is an abnormal detection, obtain the paint surface image of the corresponding vehicle paint surface; supplement the paint surface image and the paint surface detection result to the vehicle model.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] Through the mutual cooperation between modules, the AI algorithm replaces the expensive equipment and manual services of 4S stores / professional inspection agencies, reducing the single inspection cost, enabling individual car owners to obtain professional inspection reports without paying high fees; by docking with the acquisition unit, real-time paint surface images of the user's vehicle are collected, realizing real-time paint surface detection of the user's vehicle, and helping users to promptly discover paint surface abnormalities; for the situation where the user does not have a paint surface acquisition tutorial, an intelligent adjustment method is adopted to facilitate the user to collect paint surface images and avoid long waiting times for the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following-described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0043] Figure 1 It is a block diagram of the principle of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0045] As Figure 1 shown, the vehicle paint surface detection and processing system based on AI includes a platform end and a user end;

[0046] The platform end is maintained and used by the platform party, and includes a paint surface detection module and a detection guidance module;

[0047] The paint surface detection module is used to perform paint surface detection on the vehicle, obtain vehicle information and paint surface images of the vehicle to be inspected; analyze the vehicle information and paint surface images to obtain a paint surface detection result; and send the paint surface detection result to the corresponding user end.

[0048] In one embodiment, the vehicle information and paint surface images are analyzed by the platform party according to the existing AI technology and paint surface detection technology, such as establishing a corresponding intelligent detection model for intelligent detection.

[0049] Exemplarily, based on the lightweight YOLOv8-Tiny model, common defects such as scratches and runs are located; for suspicious areas, the Transformer-3D model is called to calculate the defect depth, area, and repair cost by combining multi-modal data; for the user's vehicle model, the model is fine-tuned with a small number of samples; an interactive 3D model is generated through WebGL, and the user can rotate to view the defect location and simulate the effect after repair.

[0050] The detection guidance module is used to set the paint surface acquisition tutorial for the corresponding vehicle, mark the corresponding vehicle information on the paint surface acquisition tutorial, and store the paint surface acquisition tutorial in the tutorial library; the tutorial library is a database for storing data.

[0051] In one embodiment, the paint surface acquisition tutorial can be set by the platform party according to the existing method for the corresponding vehicle's paint surface acquisition tutorial.

[0052] In one embodiment, the setting of the paint surface acquisition tutorial includes:

[0053] Real-time obtain all kinds of vehicles available on the current market to form a vehicle list; according to the vehicle list, real-time collect the paint surface feature sets of the corresponding vehicles. The paint surface feature sets are composed of various paint surface features. The paint surface features refer to various features related to paint surface recognition, which are used to judge whether the same acquisition method can be used for subsequent acquisition, such as color, texture, gloss, composition, and other related paint surface features;

[0054] Judge whether the corresponding paint surface feature sets meet the tutorial usage standard. The tutorial usage standard is that the paint surface images can be acquired using the same paint surface acquisition tutorial and have no impact on subsequent paint surface detection; it can be directly combined with the historical paint surface images of the corresponding vehicle for detection and judgment, and determine whether it meets the tutorial usage standard according to the detection results;

[0055] Classify the vehicle information that meets the tutorial usage standard into one category to obtain the corresponding vehicle paint surface classification; the platform party sets the corresponding paint surface acquisition tutorial for the corresponding vehicle paint surface classification; generally, after setting a paint surface acquisition tutorial, fine-tune it according to the differences in the paint surface feature sets.

[0056] In one embodiment, when there is a new vehicle paint surface classification, identify the paint surface feature set corresponding to the vehicle, match a vehicle paint surface classification with the highest similarity according to the paint surface feature set; identify the paint surface acquisition tutorial corresponding to the vehicle paint surface classification, and adjust the paint surface acquisition tutorial according to the paint surface feature set of the vehicle to obtain the paint surface acquisition tutorial for the new vehicle paint surface classification; it can be adjusted manually or intelligently based on existing intelligent technologies.

[0057] For the situation where there is no paint surface acquisition tutorial for user matching, an intelligent adjustment method is adopted to facilitate the user to collect paint surface images and avoid long waiting times for the user.

[0058] The user terminal is for user use and can be set in the form of a small program, app, web page, etc.; it includes a paint surface acquisition module and a result display module.

[0059] The paint surface acquisition module is used to collect the paint surface image of the user's vehicle, obtain the vehicle information of the user's vehicle, and send the vehicle information and the paint surface image to the platform terminal.

[0060] In one embodiment, the method for collecting the paint surface image includes:

[0061] Obtain the vehicle information of the user's vehicle, match the corresponding paint surface acquisition tutorial from the tutorial library according to the vehicle information; display the paint surface acquisition tutorial to the user.

[0062] The user collects an image of the vehicle paint surface according to the paint surface acquisition tutorial to obtain a paint surface image.

[0063] In one embodiment, the paint surface image collected by the user can also be verified to ensure the quality of the paint surface image.

[0064] In one embodiment, the paint surface acquisition module further includes a docking acquisition unit, and the docking acquisition unit is used to dock with image acquisition systems such as the vehicle panoramic camera system and the 360° surround view monitoring system of the user's vehicle, obtain the vehicle acquisition image of the user's vehicle in real time by the image acquisition system, and process the vehicle acquisition image to obtain a vehicle paint surface image.

[0065] Real-time paint surface image acquisition of the user's vehicle is achieved through the docking acquisition unit, and real-time paint surface detection of the user's vehicle is realized to help the user discover paint surface abnormalities in time.

[0066] In one embodiment, to process the acquired image, existing image processing technologies can be used to crop the vehicle acquisition image to obtain the paint surface image of the user's vehicle.

[0067] In one embodiment, processing the acquired image includes:

[0068] Locate the captured images according to the position of the user's vehicle, calibrate the quality of the captured images at the corresponding positions of the user's vehicle, and obtain the quality calibration results of the captured images at the corresponding positions, including qualified quality calibration and unqualified quality calibration; that is, both exceeding the standards in terms of image quality and pollutants are regarded as unqualified quality calibration. The captured images with unqualified quality calibration will affect subsequent detection and analysis, so they will be excluded later. Because it is dynamic real-time capture and detection, there is a time span. After the current image is excluded, detection is performed based on the subsequent images that meet the requirements, with a short interval, improving the detection accuracy; perform corresponding processing on the captured images according to the quality calibration results, such as marking for no detection and analysis, cropping and exclusion, etc.; and can also record and provide feedback to the user.

[0069] In one embodiment, the quality calibration of the captured images at the corresponding positions of the user's vehicle can be performed based on existing image quality calibration methods.

[0070] In one embodiment, the quality calibration of the captured images at the corresponding positions of the user's vehicle includes:

[0071] Establish a quality calibration model, and the expression of the quality calibration model is:

[0072]

[0073] In the formula: s is the input data, representing the captured image; use the historical image marking training set for training to determine various pollutant characteristics and characteristics of unqualified image quality on the vehicle, and summarize and set the training set; so that it can be judged later whether the captured images at the corresponding positions meet the image quality requirements; the output data is the quality evaluation value ZP(s), and the quality evaluation value is 1 or 0.

[0074] Analyze the captured images at the corresponding positions through the quality calibration model to obtain the quality evaluation values at the corresponding positions.

[0075] When the quality evaluation value is 1, the quality calibration result is qualified quality calibration.

[0076] When the quality evaluation value is 0, the quality calibration result is unqualified quality calibration.

[0077] In one embodiment, generate a corresponding image quality model according to the quality calibration results of each position of the user's vehicle obtained in real time, and display the image quality model to the user in real time.

[0078] The image quality model is generated according to the vehicle paint surface and is a visual data model, which can be a two-dimensional or three-dimensional model.

[0079] Generation of the image quality model:

[0080] Generate a visualization model of the user's vehicle based on vehicle information, marked as the vehicle model; respectively set the display methods corresponding to qualified and unqualified quality calibrations, such as green, red, etc.

[0081] Obtain the quality calibration results of the vehicle at the corresponding position in real time, and adjust the display of the paint area in the vehicle model according to the display method corresponding to the quality calibration results. The paint area is the area corresponding to the vehicle paint surface. Mark the vehicle model after the display adjustment as the image quality model; subsequently, perform dynamic updates according to the quality calibration results.

[0082] In one embodiment, corresponding optimization measures can also be recommended to the user according to the image quality model, such as replacing the device with higher image acquisition ability, performing repair and adjustment through image repair algorithms, etc.

[0083] The result display module is used to display the paint surface detection results. Generate a vehicle model according to vehicle information, obtain the paint surface detection results sent by the platform party in real time, and adjust the display of the vehicle model according to the paint surface detection results; display the vehicle model after the display adjustment to the user, and perform real-time updates on the vehicle model according to the paint surface detection results.

[0084] Adjust the display of the vehicle model according to the paint surface detection results, distinguish and display those with unqualified paint surface detections, and supplement the corresponding paint surface detection results for the user to view.

[0085] Exemplarily, adjusting the display of the vehicle model according to the paint surface detection results includes:

[0086] Preset the display adjustment methods of the vehicle model for different paint surface detection results. Multiple display adjustment methods can be preset, and the display adjustment methods corresponding to different paint surface detection results can be selected according to the user's preference.

[0087] Identify the paint surface detection results in real time, match the corresponding display adjustment methods according to the paint surface detection results, and adjust the display of the vehicle model according to the display adjustment methods.

[0088] In one embodiment, when the paint surface detection result is an abnormal detection, obtain the paint surface image of the corresponding paint surface, supplement the paint surface image and the paint surface detection result into the vehicle model for the user to check, and optimize and learn and adjust the paint surface detection technology according to the user's check result.

[0089] The above formulas are all calculated by removing the dimension and taking their numerical values. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through a large amount of data simulation.

[0090] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An AI-based vehicle paint surface detection and processing system, characterized in that, It includes a platform end and a user end; The platform end includes a paint surface detection module and a detection guidance module; The paint surface detection module is used to perform paint surface detection on a vehicle to be inspected, obtain the vehicle information and paint surface images of the vehicle to be inspected; analyze the vehicle information and paint surface images to obtain a paint surface detection result; and send the paint surface detection result to the corresponding user end; The detection guidance module is used to set a paint surface acquisition tutorial for the corresponding vehicle, mark the corresponding vehicle information for the paint surface acquisition tutorial, and store the paint surface acquisition tutorial in a tutorial library; The user end includes a paint surface acquisition module and a result display module; The paint surface acquisition module is used to acquire the paint surface images of the user's vehicle and send the vehicle information and paint surface images to the platform end; The result display module is used to display the paint surface detection result, generate a vehicle model according to the vehicle information, obtain the paint surface detection result in real time, and perform display adjustment on the vehicle model according to the paint surface detection result; and display the vehicle model after the display adjustment to the user.

2. The AI-based vehicle paint surface detection and processing system according to claim 1, wherein, The setting of the paint surface acquisition tutorial includes: Setting a vehicle list, where the vehicle list is used to count various vehicle information; and acquiring the paint surface feature set of the corresponding vehicle in real time according to the vehicle list; Judging whether the corresponding vehicles meet the tutorial usage standard according to the paint surface feature set, where the tutorial usage standard is that the paint surface images can be acquired using the same paint surface acquisition tutorial; Classifying the vehicle information that meets the tutorial usage standard into one category to obtain the corresponding vehicle paint surface classification; and setting the corresponding paint surface acquisition tutorial for the vehicle paint surface classification by the platform side.

3. The AI-based vehicle paint surface detection and processing system according to claim 2, wherein When there is a new vehicle paint surface classification, identify the paint surface feature set of the corresponding vehicle, match a vehicle paint surface classification with the highest similarity according to the paint surface feature set; identify the paint surface acquisition tutorial of the vehicle paint surface classification, and adjust the paint surface acquisition tutorial according to the paint surface feature set to obtain the paint surface acquisition tutorial of the new vehicle paint surface classification.

4. The AI-based vehicle paint surface detection and processing system according to claim 1, characterized in that, The method for acquiring paint surface images includes: Obtaining the vehicle information of the user's vehicle, and matching the corresponding paint surface acquisition tutorial from the tutorial library according to the vehicle information; and displaying the paint surface acquisition tutorial to the user; The user acquires paint surface images of the vehicle paint surface according to the paint surface acquisition tutorial to obtain paint surface images.

5. The AI-based vehicle paint surface detection and processing system according to claim 1, wherein The paint surface acquisition module further includes a docking acquisition unit, where the docking acquisition unit is used to dock with the image acquisition system of the user's vehicle, and the image acquisition system is used to acquire images of the user's vehicle; Obtaining the vehicle acquisition images of the user's vehicle by the image acquisition system in real time, and processing the vehicle acquisition images to obtain vehicle paint surface images.

6. The AI-based vehicle paint surface detection and processing system according to claim 5, wherein, Processing the acquired images includes: Positioning the acquired images according to the position of the user's vehicle, calibrating the quality of the acquired images at the corresponding positions of the user's vehicle to obtain the quality calibration result of the acquired images at the corresponding positions, where the quality calibration result includes qualified quality calibration and unqualified quality calibration; and performing corresponding processing on the acquired images according to the quality calibration result.

7. The AI-based vehicle paint surface detection and processing system according to claim 6, characterized in that, Calibrating the quality of the acquired images at the corresponding positions of the user's vehicle includes: Establishing a quality calibration model, and the expression of the quality calibration model is: Where: s is the input data, representing the acquired image; the output data is the quality evaluation value ZP(s), and the quality evaluation value is 1 or 0; Analyze the acquired image at the corresponding position through the quality calibration model to obtain the quality evaluation value at the corresponding position; When the quality evaluation value is 1, the quality calibration result is that the quality calibration is qualified; When the quality evaluation value is 0, the quality calibration result is that the quality calibration is unqualified.

8. The AI-based vehicle paint surface detection and processing system according to claim 6, wherein Generate the corresponding image quality model according to the quality calibration results of each position of the user's vehicle obtained in real time, and display the image quality model to the user in real time.

9. The AI-based vehicle paint surface detection and processing system according to claim 8, wherein Generation of the image quality model: Generate a visualization model of the user's vehicle according to the vehicle information, marked as the vehicle model; set the corresponding display methods for qualified and unqualified quality calibrations respectively; Obtain the quality calibration results of the corresponding positions of the vehicle in real time, adjust the display of the paint area in the vehicle model according to the display methods corresponding to the quality calibration results to obtain the image quality model; and dynamically update the image quality model according to the quality calibration results.

10. The AI-based vehicle paint surface detection and processing system according to claim 1, wherein, Adjust the display of the vehicle model according to the paint surface detection results, including: Preset the display adjustment methods for different paint surface detection results for the vehicle model; identify the paint surface detection results in real time, match the corresponding display adjustment methods according to the paint surface detection results, and adjust the display of the vehicle model according to the display adjustment methods; When the paint surface detection result is an abnormal detection, obtain the paint surface image of the corresponding vehicle paint surface; supplement the paint surface image and the paint surface detection result to the vehicle model.

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