A tire detection method, device, electronic equipment and system

Abstract

The present invention relates to the field of tire detection technology. The present invention provides a tire detection method, device, electronic equipment and system, which perform comprehensive detection by acquiring tire images, including tire surface images and X-ray images. The present invention first performs image preprocessing, automatically identifies and marks tire defects, and generates digital twin images to assist in analysis. Next, the tire is inspected in a circular shape by an industrial camera and an X-ray camera to collect comprehensive tire inspection data. Then, the data is preprocessed, feature extracted, matched and clustered to screen out valid data of tire defect information. Finally, these data are used to optimize a preset tire detection model to improve the accuracy and adaptability of the model. The present invention realizes comprehensive and accurate detection of tires, can promptly detect defects such as tiny cracks, and reduces the risk of traffic accidents caused by tire problems.

Description

Technical Field

[0001] The present invention relates to the technical field of tire detection technology, and in particular to a tire detection method, device, electronic equipment and system. Background Art

[0002] With the rapid development of science and technology, digital technology has been widely used in various fields, and tire quality inspection is no exception. At present, tire quality inspection is gradually realizing digital transformation by using advanced machine vision, sensor technology and deep learning algorithms. The development of these technologies provides strong technical support for quality control in the tire production process, making the inspection process more automated, accurate and efficient. Digital inspection methods can not only quickly identify defects on the tire surface, but also accurately evaluate the internal structure and performance of the tire, thereby ensuring the safety and reliability of the tire.

[0003] In the tire production process, the application of digital inspection technology is mainly reflected in two aspects. First, the image of the tire surface is captured by a high-resolution camera, and then the image processing technology is used to carefully analyze the tire surface to automatically detect potential defects such as bubbles and cracks. Secondly, through integrated sensor technology, various performance indicators of the tire during the production process, such as air pressure and temperature, are monitored in real time to ensure that each tire meets the quality standards. These data are not only used for immediate quality control, but also for subsequent product traceability and process improvement.

[0004] Although digital inspection technology has shown significant advantages in tire quality inspection, there are still the following technical pain points. The algorithms and models used in tire quality inspection require a large amount of historical data as accumulation, but the production design of tires is advancing with the times, and the styles of tires are constantly updated. When there is erroneous information in the data used to train the tire inspection model, the tire inspection model will be inaccurate. When new tires need to be inspected, the tire inspection model cannot identify the tiny cracks of the new tires because the patterns of the new tires are inconsistent with the tire patterns in the historical data. Summary of the invention

[0005] The purpose of the present invention is to provide a tire detection method, device, electronic device and system to solve the problem that when there is erroneous information in the data for training the tire detection model, the tire detection model will be not accurate enough. When a new tire needs to be detected, the tire detection model cannot identify the tiny cracks of the new tire because the pattern of the new tire is inconsistent with the tire pattern in the historical data.

[0006] In a first aspect, the present invention provides a tire detection method, comprising:

[0007] Step S101: Acquire a tire image, where the tire image includes a tire surface image and a tire X-ray image;

[0008] Step S102: performing image preprocessing on the acquired tire image to obtain preprocessed tire image data, using a preset tire detection model to automatically identify and mark tire defects on the preprocessed tire image data to obtain tire defect information, and simultaneously substituting the preprocessed tire image data into a preset digital twin model to generate a digital twin image of the tire;

[0009] Step S103: Substitute the tire defect information into the digital twin image of the tire, display the position corresponding to the tire defect information in the digital twin image of the tire, and send a moving command to the industrial camera and the X-ray camera device according to the position corresponding to the tire defect information, so that the industrial camera and the X-ray camera device move to the position corresponding to the tire defect information, and continuously shoot the position corresponding to the tire defect information through the moving command of the industrial camera and the X-ray camera device to obtain a position image data set corresponding to the tire defect information;

[0010] Step S104: Calculate the outer diameter of the tire according to the length of the tire, take the outer diameter of the tire as the tire inspection path distance, take the position corresponding to the tire defect information as the tire defect inspection starting point and final inspection end point, send a drive command to the industrial camera, drive the industrial camera to use the outer diameter of the tire as the motion trajectory, perform a circular inspection around the tire, and obtain tire inspection data;

[0011] Step S105: Preprocess the tire inspection data to obtain tire inspection preprocessed data, extract tire features from the tire inspection preprocessed data to obtain tire inspection data feature extraction results, match the tire inspection data feature extraction results with the position image data set corresponding to the tire defect information to obtain tire defect feature matching results, perform cluster analysis on the tire defect feature matching results, filter out valid tire defect information data from the cluster analysis results, use the valid tire defect information data to train a preset tire detection model to obtain an optimized tire detection model, and use the optimized tire detection model to analyze and process tire images to obtain tire detection results.

[0012] Furthermore, the tire detection method provided by the present invention further includes:

[0013] Obtain the standard air pressure corresponding to each temperature value of the tire, and receive the real-time air pressure data of the tire collected by the sensor end, where the real-time air pressure of the tire includes the real-time internal air pressure of the tire and the real-time external air pressure of the tire;

[0014] Compare the real-time tire pressure data with the standard tire pressure to obtain tires that meet the standard tire pressure, and perform image detection at different tire pressures for tires of companies that meet tire standards;

[0015] According to the standard air pressure corresponding to each temperature value and the real-time tire air pressure data, a tire air adjustment sequence table is established, wherein the tire air adjustment sequence table includes the standard air pressure corresponding to each temperature value and the real-time tire air pressure;

[0016] Select one of the temperature values ​​in the tire gas adjustment sequence table, and adjust the tire pressure based on the temperature value according to the tire gas adjustment sequence table. Maintain the tire pressure for 40 seconds after each tire pressure adjustment. During the 40-second period of maintaining the tire pressure, detect the tire after the pressure adjustment by using an industrial camera and an X-ray camera to obtain first pressure-adjusted tire detection collection data;

[0017] The remaining temperature values ​​in the tire gas adjustment sequence table are selected in turn, and the corresponding tire pressure adjustment is completed in turn. The tire after the pressure adjustment is detected by an industrial camera and an X-ray camera to obtain a pressure-adjusted tire detection collection data set. The model is trained by the pressure-adjusted tire detection collection data set to obtain a pressure-adjusted tire image detection model. The image data of the tire that meets the standard tire pressure is substituted into the pressure-adjusted tire image detection model for processing, and the appearance detection result of the tire in the pressure-adjusted state is output.

[0018] Furthermore, the tire detection method provided by the present invention further includes:

[0019] Based on the appearance inspection results of the tire under the air pressure adjustment state, the appearance inspection and the inspection results under the air pressure adjustment state are compared and analyzed. If the tire performs normally in the inspections of texture, color, hardness and air pressure adjustment state, it is judged to be of good quality; conversely, if there are multiple abnormalities, it should be judged to be of poor quality. If the tire quality is poor, a quality warning information is generated.

[0020] Furthermore, the tire detection method provided by the present invention, step S104: according to the length of the tire, the outer diameter of the tire is calculated, the outer diameter of the tire is used as the tire inspection path distance, the position corresponding to the tire defect information is used as the tire defect inspection starting point and the final inspection end point, and a drive command is sent to the industrial camera to drive the industrial camera to use the outer diameter of the tire as the motion trajectory to perform a circular inspection around the tire to obtain tire inspection data, including:

[0021] The outer diameter D of the tire is calculated by the following formula: D = wheel diameter + tire width × aspect ratio × 2;

[0022] The calculated tire outer diameter is used as the inspection path distance, and the industrial camera will perform circular inspection along this path;

[0023] The positions corresponding to the tire defect information will be set as the starting and ending points of the inspection. After completing the inspection around the tire, the industrial camera can be adjusted to the second angle to perform another surrounding inspection on the tire. When the second angle surrounding inspection is completed, the tire will be inspected at the third angle. The second angle is located on the left side of the starting point of the inspection, and the third angle is located on the right side of the starting point of the inspection.

[0024] In a second aspect, a tire detection device is provided, using the tire detection method, including:

[0025] An acquisition unit, which acquires a tire image, wherein the tire image includes a tire surface image and a tire X-ray image;

[0026] A data preprocessing unit performs image preprocessing on the acquired tire image to obtain preprocessed tire image data, uses a preset tire detection model to automatically identify and mark tire defects on the preprocessed tire image data to obtain tire defect information, and simultaneously substitutes the preprocessed tire image data into a preset digital twin model to generate a digital twin image of the tire;

[0027] The data analysis unit substitutes the tire defect information into the digital twin image of the tire, displays the position corresponding to the tire defect information in the digital twin image of the tire, and sends a moving command to the industrial camera and the X-ray camera device according to the position corresponding to the tire defect information, so that the industrial camera and the X-ray camera device move the moving command to the position corresponding to the tire defect information, and continuously shoots the position corresponding to the tire defect information through the moving command of the industrial camera and the X-ray camera device to obtain a position image data set corresponding to the tire defect information;

[0028] The tire inspection unit calculates the outer diameter of the tire according to the length of the tire, takes the outer diameter of the tire as the tire inspection path distance, takes the position corresponding to the tire defect information as the tire defect inspection starting point and final inspection end point, sends a drive command to the industrial camera, drives the industrial camera to use the outer diameter of the tire as the movement trajectory, performs a circular inspection around the tire, and obtains tire inspection data;

[0029] The tire detection model optimization unit performs data preprocessing on the tire inspection data to obtain tire inspection preprocessed data, performs tire feature extraction on the tire inspection preprocessed data to obtain tire inspection data feature extraction results, matches the tire inspection data feature extraction results with the position image data set corresponding to the tire defect information to obtain tire defect feature matching results, performs cluster analysis on the tire defect feature matching results, screens out valid data of the tire defect information from the cluster analysis results, uses the valid data of the tire defect information to train a preset tire detection model to obtain an optimized tire detection model, and the optimized tire detection model is used to analyze and process tire images to obtain tire detection results.

[0030] In a third aspect, a tire detection electronic device includes:

[0031] A processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction implements the steps of a tire detection method when executed by the processor.

[0032] In a fourth aspect, a tire detection system is provided, which uses the tire detection method, including: a server end, a background control end, a device end, a detection device end, and a mobile electronic device end, wherein the server end establishes communication connections with the background control end, the device end, the detection device end, and the mobile electronic device end respectively;

[0033] The server side obtains tire images, which include tire surface images and tire X-ray images;

[0034] The server performs image preprocessing on the acquired tire image to obtain preprocessed tire image data, uses a preset tire detection model to automatically identify and mark tire defects on the preprocessed tire image data to obtain tire defect information, and simultaneously substitutes the preprocessed tire image data into a preset digital twin model to generate a digital twin image of the tire;

[0035] The server side substitutes the tire defect information into the digital twin image of the tire, displays the position corresponding to the tire defect information in the digital twin image of the tire, and sends a moving command to the industrial camera and the X-ray camera device according to the position corresponding to the tire defect information, so that the industrial camera and the X-ray camera device move the moving command to the position corresponding to the tire defect information, and continuously shoots the position corresponding to the tire defect information through the moving command of the industrial camera and the X-ray camera device, so as to obtain a position image data set corresponding to the tire defect information;

[0036] The server calculates the outer diameter of the tire based on the length of the tire, takes the outer diameter of the tire as the distance of the tire inspection path, takes the position corresponding to the tire defect information as the starting point and the final inspection end point of the tire defect inspection, and sends a driving command to the industrial camera to drive the industrial camera to use the outer diameter of the tire as the movement trajectory and perform a circular inspection around the tire to obtain tire inspection data;

[0037] The server side performs data preprocessing on the tire inspection data to obtain tire inspection preprocessed data, performs tire feature extraction on the tire inspection preprocessed data to obtain tire inspection data feature extraction results, matches the tire inspection data feature extraction results with the position image data set corresponding to the tire defect information to obtain tire defect feature matching results, performs cluster analysis on the tire defect feature matching results, filters out valid data of tire defect information from the cluster analysis results, uses the valid data of tire defect information to train a preset tire detection model to obtain an optimized tire detection model, and the optimized tire detection model is used to analyze and process tire images to obtain tire detection results.

[0038] The beneficial effects of the present invention are as follows:

[0039] Automation and precision: By combining a high-resolution camera to capture tire surface images and using image processing technology to automatically detect tire defects, the present invention achieves automation and precision in the tire detection process. This not only improves detection efficiency, but also reduces errors caused by human operation, making tire detection more accurate and reliable.

[0040] High efficiency: Integrated sensor technology monitors tire performance indicators in real time and can quickly provide feedback on tire status, thereby promptly identifying and addressing potential problems. This high efficiency helps improve the real-time and response speed of tire testing and ensures that tires operate in optimal conditions.

[0041] Safety and reliability: Through the preset tire detection model and digital twin model, the present invention can identify tire defects more accurately and even quickly identify tiny cracks in new tires, thereby enhancing the safety and reliability of tires and reducing the risk of traffic accidents caused by tire problems.

[0042] Comprehensiveness: The present invention further improves the comprehensiveness of tire inspection by using industrial cameras to inspect the tire in a circular pattern. This all-round inspection method ensures that every part of the tire is fully inspected, so that various potential problems can be discovered and dealt with in a timely manner.

[0043] In summary, the beneficial effects of the present invention are mainly reflected in automation, accuracy, efficiency, safety, reliability and comprehensiveness, etc. These advantages make the present invention have broad application prospects and important practical value in the field of tire detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 A schematic flow chart of a tire detection method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0047] First, see Figure 1 ,The present invention provides a tire detection method, comprising:

[0048] Step S101: Acquire a tire image, where the tire image includes a tire surface image and a tire X-ray image;

[0049] The specific process of obtaining the tire image in step S101 includes using an industrial camera to capture the surface image of the tire, and using an X-ray camera to obtain an X-ray image of the tire.

[0050] Step S102: performing image preprocessing on the acquired tire image to obtain preprocessed tire image data, using a preset tire detection model to automatically identify and mark tire defects on the preprocessed tire image data to obtain tire defect information, and simultaneously substituting the preprocessed tire image data into a preset digital twin model to generate a digital twin image of the tire;

[0051] Image preprocessing: First, the acquired tire images are preprocessed. The purpose of this step is to remove noise and unnecessary background information in the image, so as to highlight the characteristics of the tire itself. Preprocessing operations include image filtering, contrast enhancement, color correction, etc. to improve image quality and clarity. The tire image data obtained after preprocessing will serve as the basis for subsequent analysis.

[0052] Automatically identify and mark tire defects: Next, use the preset tire detection model to automatically identify and mark the preprocessed tire image data. This model is built based on machine learning or deep learning algorithms and can accurately identify tiny cracks and other defects on tires. Through the automatic identification and marking of the model, we can get the specific information and location of tire defects.

[0053] Generate digital twin images: While identifying and marking tire defects, the system will also synchronously substitute the pre-processed tire image data into the preset digital twin model. The digital twin model is a virtual tire model that can simulate the actual situation of the tire, including its physical properties and behavior. By substituting the image data of the actual tire into the model, a digital twin image of the tire can be generated. This image not only shows the appearance of the tire, but also reflects its internal structure and existing defects. Researchers can better understand and evaluate the defect status of the tire by analyzing the digital twin image, providing a basis for subsequent repair or replacement decisions.

[0054] Step S103: Substitute the tire defect information into the digital twin image of the tire, display the position corresponding to the tire defect information in the digital twin image of the tire, and send a moving command to the industrial camera and the X-ray camera device according to the position corresponding to the tire defect information, so that the industrial camera and the X-ray camera device move to the position corresponding to the tire defect information, and continuously shoot the position corresponding to the tire defect information through the moving command of the industrial camera and the X-ray camera device to obtain a position image data set corresponding to the tire defect information;

[0055] Displaying the location of tire defects in the digital twin image: First, the previously identified tire defect information is substituted into the digital twin image of the tire. This enables the specific location corresponding to the tire defect information to be intuitively displayed on the digital twin image, making it easier to observe and locate.

[0056] Mobile industrial cameras and X-ray equipment: Based on the tire defect location shown in the digital twin image, the system will issue a movement command to the industrial camera and X-ray equipment. After receiving the command, these devices will accurately move to the location corresponding to the tire defect information for subsequent detailed observation and photography.

[0057] Continuously photograph tire defect locations: When industrial cameras and X-ray equipment move to designated locations, they will continuously photograph tire defect information. Through this continuous photographing, a set of location image data sets corresponding to tire defect information can be obtained. These data sets provide rich information and support for subsequent tire defect analysis, maintenance decisions, etc.

[0058] Step S104: Calculate the outer diameter of the tire according to the length of the tire, take the outer diameter of the tire as the tire inspection path distance, take the position corresponding to the tire defect information as the tire defect inspection starting point and final inspection end point, send a drive command to the industrial camera, drive the industrial camera to use the outer diameter of the tire as the motion trajectory, perform a circular inspection around the tire, and obtain tire inspection data;

[0059] The calculated tire outer diameter is used as the distance of the tire inspection path, which means that during the inspection process, the industrial camera will move in a circle along this path to ensure that the tire surface can be fully covered and inspected.

[0060] Set the starting and ending points of the tire defect inspection: Based on the locations of the previously identified tire defect information, we will determine the starting and ending points of the inspection. This setting ensures that the inspection process focuses on the potential problem areas of the tire in a targeted manner, improving the efficiency and accuracy of the inspection.

[0061] Drive the industrial camera to perform circular inspection: Send a drive command to the industrial camera to make it perform circular inspection around the tire with the outer diameter of the tire as the movement trajectory. During the inspection process, the industrial camera will capture image data on the tire surface, which will be used for subsequent defect analysis and identification.

[0062] Step S105: Preprocess the tire inspection data to obtain tire inspection preprocessed data, extract tire features from the tire inspection preprocessed data to obtain tire inspection data feature extraction results, match the tire inspection data feature extraction results with the position image data set corresponding to the tire defect information to obtain tire defect feature matching results, perform cluster analysis on the tire defect feature matching results, filter out valid tire defect information data from the cluster analysis results, use the valid tire defect information data to train a preset tire detection model to obtain an optimized tire detection model, and use the optimized tire detection model to analyze and process tire images to obtain tire detection results.

[0063] The extracted tire features are matched with known tire defect patterns. Through matching, features related to tire defects can be identified, providing clues for further defect classification and identification.

[0064] Cluster analysis: Cluster analysis is performed on the matched tire defect features to classify similar defect features. This helps to understand the type, distribution and severity of tire defects and provides important information for tire maintenance and management.

[0065] Effective data screening: Based on cluster analysis, effective data that can accurately reflect tire defects are screened out. These data will provide strong support for subsequent model training and improve the accuracy and generalization ability of the model.

[0066] Model training: Use the filtered valid data to train the preset tire inspection model. Through training, the model can more accurately identify and classify tire defects, thereby obtaining an optimized tire inspection model.

[0067] Tire inspection results: Finally, the optimized tire inspection model is used to analyze and process the new tire image. The inspection results will include information such as whether the tire has defects, the type, location and severity of the defects, providing important basis for tire repair and replacement.

[0068] Specifically, the tire detection method provided by the present invention also includes:

[0069] Obtain the standard air pressure corresponding to each temperature value of the tire, and receive the real-time air pressure data of the tire collected by the sensor end, where the real-time air pressure of the tire includes the real-time internal air pressure of the tire and the real-time external air pressure of the tire;

[0070] Compare the real-time tire pressure data with the standard tire pressure to obtain tires that meet the standard tire pressure, and perform image detection at different tire pressures for tires of companies that meet tire standards;

[0071] According to the standard air pressure corresponding to each temperature value and the real-time tire air pressure data, a tire air adjustment sequence table is established, wherein the tire air adjustment sequence table includes the standard air pressure corresponding to each temperature value and the real-time tire air pressure;

[0072] Select one of the temperature values ​​in the tire gas adjustment sequence table, and adjust the tire pressure based on the temperature value according to the tire gas adjustment sequence table. Maintain the tire pressure for 40 seconds after each tire pressure adjustment. During the 40-second period of maintaining the tire pressure, detect the tire after the pressure adjustment by using an industrial camera and an X-ray camera to obtain first pressure-adjusted tire detection collection data;

[0073] The remaining temperature values ​​in the tire gas adjustment sequence table are selected in turn, and the corresponding tire pressure adjustment is completed in turn. The tire after the pressure adjustment is detected by an industrial camera and an X-ray camera to obtain a pressure-adjusted tire detection collection data set. The model is trained by the pressure-adjusted tire detection collection data set to obtain a pressure-adjusted tire image detection model. The image data of the tire that meets the standard tire pressure is substituted into the pressure-adjusted tire image detection model for processing, and the appearance detection result of the tire in the pressure-adjusted state is output.

[0074] Specifically, the tire detection method provided by the present invention also includes:

[0075] Based on the appearance inspection results of the tire under the air pressure adjustment state, the appearance inspection and the inspection results under the air pressure adjustment state are compared and analyzed. If the tire performs normally in the inspections of texture, color, hardness and air pressure adjustment state, it is judged to be of good quality; conversely, if there are multiple abnormalities, it should be judged to be of poor quality. If the tire quality is poor, a quality warning information is generated.

[0076] Specifically, the tire detection method provided by the present invention, step S104: according to the length of the tire, the outer diameter of the tire is calculated, the outer diameter of the tire is used as the tire inspection path distance, the position corresponding to the tire defect information is used as the tire defect inspection starting point and the final inspection end point, and a driving command is sent to the industrial camera to drive the industrial camera to use the outer diameter of the tire as the motion trajectory to perform a circular inspection around the tire to obtain tire inspection data, including:

[0077] The outer diameter D of the tire is calculated by the following formula: D = wheel diameter + tire width × aspect ratio × 2;

[0078] The calculated tire outer diameter is used as the inspection path distance, and the industrial camera will perform circular inspection along this path;

[0079] The positions corresponding to the tire defect information will be set as the starting and ending points of the inspection. After completing the inspection around the tire, the industrial camera can be adjusted to the second angle to perform another surrounding inspection on the tire. When the second angle surrounding inspection is completed, the tire will be inspected at the third angle. The second angle is located on the left side of the starting point of the inspection, and the third angle is located on the right side of the starting point of the inspection.

[0080] In a second aspect, a tire detection device is provided, using the tire detection method, including:

[0081] An acquisition unit, which acquires a tire image, wherein the tire image includes a tire surface image and a tire X-ray image;

[0082] A data preprocessing unit performs image preprocessing on the acquired tire image to obtain preprocessed tire image data, uses a preset tire detection model to automatically identify and mark tire defects on the preprocessed tire image data to obtain tire defect information, and simultaneously substitutes the preprocessed tire image data into a preset digital twin model to generate a digital twin image of the tire;

[0083] The data analysis unit substitutes the tire defect information into the digital twin image of the tire, displays the position corresponding to the tire defect information in the digital twin image of the tire, and sends a moving command to the industrial camera and the X-ray camera device according to the position corresponding to the tire defect information, so that the industrial camera and the X-ray camera device move the moving command to the position corresponding to the tire defect information, and continuously shoots the position corresponding to the tire defect information through the moving command of the industrial camera and the X-ray camera device to obtain a position image data set corresponding to the tire defect information;

[0084] The tire inspection unit calculates the outer diameter of the tire according to the length of the tire, takes the outer diameter of the tire as the tire inspection path distance, takes the position corresponding to the tire defect information as the tire defect inspection starting point and final inspection end point, sends a drive command to the industrial camera, drives the industrial camera to use the outer diameter of the tire as the movement trajectory, performs a circular inspection around the tire, and obtains tire inspection data;

[0085] The tire detection model optimization unit performs data preprocessing on the tire inspection data to obtain tire inspection preprocessed data, performs tire feature extraction on the tire inspection preprocessed data to obtain tire inspection data feature extraction results, matches the tire inspection data feature extraction results with the position image data set corresponding to the tire defect information to obtain tire defect feature matching results, performs cluster analysis on the tire defect feature matching results, screens out valid data of the tire defect information from the cluster analysis results, uses the valid data of the tire defect information to train a preset tire detection model to obtain an optimized tire detection model, and the optimized tire detection model is used to analyze and process tire images to obtain tire detection results.

[0086] In a third aspect, a tire detection electronic device includes:

[0087] A processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction implements the steps of a tire detection method when executed by the processor.

[0088] In a fourth aspect, a tire detection system is provided, which uses the tire detection method, including: a server end, a background control end, a device end, a detection device end, and a mobile electronic device end, wherein the server end establishes communication connections with the background control end, the device end, the detection device end, and the mobile electronic device end respectively;

[0089] The server side obtains tire images, which include tire surface images and tire X-ray images;

[0090] The server performs image preprocessing on the acquired tire image to obtain preprocessed tire image data, uses a preset tire detection model to automatically identify and mark tire defects on the preprocessed tire image data to obtain tire defect information, and simultaneously substitutes the preprocessed tire image data into a preset digital twin model to generate a digital twin image of the tire;

[0091] The server side substitutes the tire defect information into the digital twin image of the tire, displays the position corresponding to the tire defect information in the digital twin image of the tire, and sends a moving command to the industrial camera and the X-ray camera device according to the position corresponding to the tire defect information, so that the industrial camera and the X-ray camera device move the moving command to the position corresponding to the tire defect information, and continuously shoots the position corresponding to the tire defect information through the moving command of the industrial camera and the X-ray camera device, so as to obtain a position image data set corresponding to the tire defect information;

[0092] The server calculates the outer diameter of the tire based on the length of the tire, takes the outer diameter of the tire as the distance of the tire inspection path, takes the position corresponding to the tire defect information as the starting point and the final inspection end point of the tire defect inspection, and sends a driving command to the industrial camera to drive the industrial camera to use the outer diameter of the tire as the movement trajectory and perform a circular inspection around the tire to obtain tire inspection data;

[0093] The server side performs data preprocessing on the tire inspection data to obtain tire inspection preprocessed data, performs tire feature extraction on the tire inspection preprocessed data to obtain tire inspection data feature extraction results, matches the tire inspection data feature extraction results with the position image data set corresponding to the tire defect information to obtain tire defect feature matching results, performs cluster analysis on the tire defect feature matching results, filters out valid data of tire defect information from the cluster analysis results, uses the valid data of tire defect information to train a preset tire detection model to obtain an optimized tire detection model, and the optimized tire detection model is used to analyze and process tire images to obtain tire detection results.

[0094] The technical solution of the present invention effectively solves the problem of inaccurate models caused by erroneous information in the data used to train tire detection models, and can also identify tiny cracks in new tires, even if their patterns are inconsistent with those in historical data. The specific solution is as follows:

[0095] Acquiring tire images: The present invention first obtains complete image information of the tire, including tire surface images and tire X-ray images, to provide a comprehensive data basis for subsequent processing and analysis.

[0096] Image preprocessing and defect identification: The acquired tire images are preprocessed to remove noise and unnecessary background information and highlight the characteristics of the tire itself. Then, the pre-set tire detection model is used to automatically identify and mark tire defects on these pre-processed image data to obtain preliminary information on tire defects. This step uses advanced image processing technology and detection models to ensure that tiny cracks and other defects in tires can be accurately identified.

[0097] Application of digital twin model: In order to analyze and understand tire defects more deeply, the present invention also introduces a digital twin model. The pre-processed tire image data is substituted into the model to generate a digital twin image of the tire. This model can simulate the actual situation of the tire and help researchers better understand and analyze the defect status of the tire.

[0098] Defect inspection and data optimization: When a defect is found in a tire, the system will send a movement command to the industrial camera and X-ray camera, so that it can accurately move to the position corresponding to the defect to take pictures, thereby obtaining detailed image data of the defect location. In addition, according to the size of the tire, the system will calculate a reasonable inspection path and conduct a circular inspection through the industrial camera to collect comprehensive tire inspection data.

[0099] After collecting enough data, the system will perform data preprocessing, feature extraction, matching and cluster analysis to filter out valid data of tire defect information. This valid data is then used to train and optimize the preset tire detection model to improve the accuracy and adaptability of the model.

[0100] Through the above technical solution, the present invention can update and optimize the tire detection model in real time, ensuring that the accuracy of the model is not affected by erroneous information in the training data. At the same time, due to the use of a digital twin model and a comprehensive inspection method, the present invention can also effectively identify tiny cracks in new tires, even if their patterns are inconsistent with the tire patterns in historical data. Such a design makes the technical solution of the present invention highly flexible and adaptable, and can meet the ever-changing market needs and the diversity of tire designs.

[0101] The actual specific implementation steps of the present invention in the process of detecting tires are as follows:

[0102] Obtain tire images: The system first obtains a complete image of the tire, including tire surface images and X-ray images, providing a data basis for subsequent analysis.

[0103] Image preprocessing and defect identification: Preprocess tire images to remove noise and background information and highlight tire features. Use preset tire inspection models to automatically identify and mark tire defects, and generate digital twin images of the tire for in-depth analysis.

[0104] Detailed defect photography: Based on the identified defect information, mark the defect location in the digital twin image. Command the industrial camera and X-ray equipment to move to the defect location for detailed photography and collect detailed image data of the defect.

[0105] Tire circumference inspection: The inspection path is calculated based on the tire size, usually the outer diameter of the tire. An industrial camera is used to inspect the tire in a circular shape, circling it multiple times from different angles to ensure a comprehensive inspection.

[0106] Data processing and model optimization: Preprocess the inspection data, extract tire features, and match them with defect information. Filter valid data through cluster analysis to train and optimize the preset tire inspection model.

[0107] These steps together constitute the process of the present invention for comprehensive and accurate tire testing. At the same time, the adaptability and accuracy of the testing method are ensured by continuously updating and optimizing the testing model.

[0108] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0109] The above-described embodiments of the present invention do not limit the protection scope of the present invention.

Claims

1. A tire detection method, characterized in that: include; Step S101: Acquire a tire image, where the tire image includes a tire surface image and a tire X-ray image; Step S102: performing image preprocessing on the acquired tire image to obtain preprocessed tire image data, using a preset tire detection model to automatically identify and mark tire defects on the preprocessed tire image data to obtain tire defect information, and simultaneously substituting the preprocessed tire image data into a preset digital twin model to generate a digital twin image of the tire; Step S103: Substitute the tire defect information into the digital twin image of the tire, display the position corresponding to the tire defect information in the digital twin image of the tire, and send a moving command to the industrial camera and the X-ray camera device according to the position corresponding to the tire defect information, so that the industrial camera and the X-ray camera device move to the position corresponding to the tire defect information, and continuously shoot the position corresponding to the tire defect information through the moving command of the industrial camera and the X-ray camera device to obtain a position image data set corresponding to the tire defect information; Step S104: Calculate the outer diameter of the tire according to the length of the tire, take the outer diameter of the tire as the tire inspection path distance, take the position corresponding to the tire defect information as the tire defect inspection starting point and final inspection end point, send a drive command to the industrial camera, drive the industrial camera to use the outer diameter of the tire as the motion trajectory, perform a circular inspection around the tire, and obtain tire inspection data; Step S105: Preprocess the tire inspection data to obtain tire inspection preprocessed data, extract tire features from the tire inspection preprocessed data to obtain tire inspection data feature extraction results, match the tire inspection data feature extraction results with the position image data set corresponding to the tire defect information to obtain tire defect feature matching results, perform cluster analysis on the tire defect feature matching results, filter out valid tire defect information data from the cluster analysis results, use the valid tire defect information data to train a preset tire detection model to obtain an optimized tire detection model, and use the optimized tire detection model to analyze and process tire images to obtain tire detection results.

2. The tire detection method according to claim 1, characterized in that: Also includes: Obtain the standard air pressure corresponding to each temperature value of the tire, and receive the real-time air pressure data of the tire collected by the sensor end, where the real-time air pressure of the tire includes the real-time internal air pressure of the tire and the real-time external air pressure of the tire; Compare the real-time tire pressure data with the standard tire pressure to obtain tires that meet the standard tire pressure, and perform image detection at different tire pressures for tires of companies that meet tire standards; According to the standard air pressure corresponding to each temperature value and the real-time tire air pressure data, a tire air adjustment sequence table is established, wherein the tire air adjustment sequence table includes the standard air pressure corresponding to each temperature value and the real-time tire air pressure; Select one of the temperature values ​​in the tire gas adjustment sequence table, and adjust the tire pressure based on the temperature value according to the tire gas adjustment sequence table. Maintain the tire pressure for 40 seconds after each tire pressure adjustment. During the 40-second period of maintaining the tire pressure, detect the tire after the pressure adjustment by using an industrial camera and an X-ray camera to obtain first pressure-adjusted tire detection collection data; The remaining temperature values ​​in the tire gas adjustment sequence table are selected in turn, and the corresponding tire pressure adjustment is completed in turn. The tire after the pressure adjustment is detected by an industrial camera and an X-ray camera to obtain a pressure-adjusted tire detection collection data set. The model is trained by the pressure-adjusted tire detection collection data set to obtain a pressure-adjusted tire image detection model. The image data of the tire that meets the standard tire pressure is substituted into the pressure-adjusted tire image detection model for processing, and the appearance detection result of the tire in the pressure-adjusted state is output.

3. The tire detection method according to claim 2, characterized in that: Also includes: Based on the appearance inspection results of the tire under the air pressure adjustment state, the appearance inspection and the inspection results under the air pressure adjustment state are compared and analyzed. If the tire performs normally in the inspections of texture, color, hardness and air pressure adjustment state, it is judged to be of good quality. If there is an abnormality, it is judged to be of poor quality and a quality warning information is generated.

4. The tire detection method according to claim 1, characterized in that: Step S104: Calculate the outer diameter of the tire according to the length of the tire, take the outer diameter of the tire as the tire inspection path distance, take the position corresponding to the tire defect information as the tire defect inspection starting point and final inspection end point, send a drive command to the industrial camera, drive the industrial camera to use the outer diameter of the tire as the motion trajectory, and perform a circular inspection around the tire to obtain tire inspection data, including: The outer diameter D of the tire is calculated by the following formula: D = wheel diameter + tire width × aspect ratio × 2; The calculated tire outer diameter is used as the inspection path distance, and the industrial camera will perform circular inspection along this path; The positions corresponding to the tire defect information will be set as the starting and ending points of the inspection. After completing the inspection around the tire, the industrial camera can be adjusted to the second angle to perform another surrounding inspection on the tire. When the second angle surrounding inspection is completed, the tire will be inspected at the third angle. The second angle is located on the left side of the starting point of the inspection, and the third angle is located on the right side of the starting point of the inspection.

5. A tire detection device, using a tire detection method as claimed in claim 1, characterized in that: include: An acquisition unit, which acquires a tire image, wherein the tire image includes a tire surface image and a tire X-ray image; A data preprocessing unit performs image preprocessing on the acquired tire image to obtain preprocessed tire image data, uses a preset tire detection model to automatically identify and mark tire defects on the preprocessed tire image data to obtain tire defect information, and simultaneously substitutes the preprocessed tire image data into a preset digital twin model to generate a digital twin image of the tire; The data analysis unit substitutes the tire defect information into the digital twin image of the tire, displays the position corresponding to the tire defect information in the digital twin image of the tire, and sends a moving command to the industrial camera and the X-ray camera device according to the position corresponding to the tire defect information, so that the industrial camera and the X-ray camera device move the moving command to the position corresponding to the tire defect information, and continuously shoots the position corresponding to the tire defect information through the moving command of the industrial camera and the X-ray camera device to obtain a position image data set corresponding to the tire defect information; The tire inspection unit calculates the outer diameter of the tire according to the length of the tire, takes the outer diameter of the tire as the tire inspection path distance, takes the position corresponding to the tire defect information as the tire defect inspection starting point and final inspection end point, sends a drive command to the industrial camera, drives the industrial camera to use the outer diameter of the tire as the movement trajectory, performs a circular inspection around the tire, and obtains tire inspection data; The tire detection model optimization unit performs data preprocessing on the tire inspection data to obtain tire inspection preprocessed data, performs tire feature extraction on the tire inspection preprocessed data to obtain tire inspection data feature extraction results, matches the tire inspection data feature extraction results with the position image data set corresponding to the tire defect information to obtain tire defect feature matching results, performs cluster analysis on the tire defect feature matching results, screens out valid data of the tire defect information from the cluster analysis results, uses the valid data of the tire defect information to train a preset tire detection model to obtain an optimized tire detection model, and the optimized tire detection model is used to analyze and process tire images to obtain tire detection results.

6. A tire detection electronic device, characterized in that: include: A processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method according to any one of claims 1 to 4.

7. A tire detection system, using a tire detection method as claimed in claim 1, characterized in that: include: The server side, the backend control side, the device side, the detection device side and the mobile electronic device side, the server side establishes communication connections with the backend control side, the device side, the detection device side and the mobile electronic device side respectively; The server side obtains tire images, which include tire surface images and tire X-ray images; The server performs image preprocessing on the acquired tire image to obtain preprocessed tire image data, uses a preset tire detection model to automatically identify and mark tire defects on the preprocessed tire image data to obtain tire defect information, and simultaneously substitutes the preprocessed tire image data into a preset digital twin model to generate a digital twin image of the tire; The server side substitutes the tire defect information into the digital twin image of the tire, displays the position corresponding to the tire defect information in the digital twin image of the tire, and sends a moving command to the industrial camera and the X-ray camera device according to the position corresponding to the tire defect information, so that the industrial camera and the X-ray camera device move the moving command to the position corresponding to the tire defect information, and continuously shoots the position corresponding to the tire defect information through the moving command of the industrial camera and the X-ray camera device, so as to obtain a position image data set corresponding to the tire defect information; The server calculates the outer diameter of the tire based on the length of the tire, takes the outer diameter of the tire as the distance of the tire inspection path, takes the position corresponding to the tire defect information as the starting point and the final inspection end point of the tire defect inspection, and sends a driving command to the industrial camera to drive the industrial camera to use the outer diameter of the tire as the movement trajectory and perform a circular inspection around the tire to obtain tire inspection data; The server side performs data preprocessing on the tire inspection data to obtain tire inspection preprocessed data, performs tire feature extraction on the tire inspection preprocessed data to obtain tire inspection data feature extraction results, matches the tire inspection data feature extraction results with the position image data set corresponding to the tire defect information to obtain tire defect feature matching results, performs cluster analysis on the tire defect feature matching results, filters out valid data of tire defect information from the cluster analysis results, uses the valid data of tire defect information to train a preset tire detection model to obtain an optimized tire detection model, and the optimized tire detection model is used to analyze and process tire images to obtain tire detection results.

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