A coating thickness detection system and method based on visual monitoring

Abstract

The present invention discloses a coating thickness detection system and method based on visual monitoring, which relates to the field of visual monitoring technology, including making a preliminary prediction of coating thickness through geometric optics; predicting coating thickness through a multi-channel neural network based on a preprocessed coating surface image and a preliminary prediction value; identifying and evaluating defects according to the predicted coating thickness to generate a final coating thickness and defect comprehensive evaluation result; and providing real-time feedback through the defect comprehensive evaluation result. The present invention significantly improves the accuracy and adaptability of the prediction by collecting coating surface images in real time and performing preprocessing, combining geometric optics to make a preliminary thickness prediction, and making an accurate coating thickness prediction through a multi-channel neural network. Through the defect recognition and evaluation module, the type, shape, complexity and location of coating surface defects are comprehensively considered, and coating defects are accurately identified and quantitatively evaluated, thereby improving the accuracy and robustness of defect detection.

Description

Technical Field

[0001] The present invention relates to the technical field of visual monitoring, and in particular to a coating thickness detection system and method based on visual monitoring. Background Art

[0002] With the continuous advancement of industrialization, coating technology has been widely used in many fields, such as automobile manufacturing, aerospace, electronic equipment, construction, etc. Coatings can not only improve the appearance of products, but also provide various functions such as corrosion resistance, oxidation resistance, and wear resistance. Therefore, the quality of the coating directly affects the performance and service life of the product. In order to ensure the uniformity and quality of the coating, accurate measurement of the coating thickness has become a vital task. Traditional coating thickness detection methods mainly rely on physical contact measurement tools, such as electromagnetic thickness gauges, eddy current flaw detectors, and ultrasonic thickness gauges. Although these methods can provide more accurate thickness data, they usually require contact with the coating surface, which may cause damage to the coating surface and are not suitable for complex curved surfaces or difficult-to-reach places.

[0003] In recent years, with the rapid development of image processing technology, machine learning and deep learning, coating thickness detection methods based on visual monitoring have become an important research direction. By collecting images of the coating surface with a high-resolution camera and analyzing the coating in combination with image processing algorithms, non-contact measurement can be achieved, which not only avoids the limitations of traditional methods, but also enables efficient real-time detection on large-scale production lines. In addition, by combining geometric optical models with image processing technology, the relationship between the intensity of reflected light and the angle of incident light is used to make a preliminary prediction of the coating thickness, further improving the accuracy of detection. However, there are two main deficiencies in the existing technology: first, most image-based thickness prediction methods rely too much on manually set parameters and rules and lack adaptability; second, existing defect detection methods usually rely on a single image feature extraction method, which cannot fully utilize the complex information of the coating surface, resulting in insufficient accuracy and robustness in defect recognition. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a coating thickness detection method based on visual monitoring to solve the problems that the image thickness prediction method lacks adaptability and the existing defect detection method cannot fully utilize the complex information of the coating surface.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a coating thickness detection method based on visual monitoring, which includes real-time acquisition of coating surface images and preprocessing of the acquired coating surface images; preliminary prediction of coating thickness through geometric optics; prediction of coating thickness through a multi-channel neural network based on the preprocessed images and preliminary prediction values; defect identification and evaluation based on the predicted coating thickness to generate a final comprehensive evaluation result of coating thickness and defects; and real-time feedback based on the comprehensive defect evaluation result.

[0008] As a preferred solution of the coating thickness detection method based on visual monitoring of the present invention, the real-time acquisition of the coating surface image is carried out in the following specific steps:

[0009] The coating surface image is captured in real time using a high-resolution camera.

[0010] As a preferred solution of the coating thickness detection method based on visual monitoring of the present invention, the preprocessing of the collected image of the coating surface is carried out in the following specific steps:

[0011] The collected images of the coating surface are subjected to denoising, image enhancement, illumination equalization, image segmentation, image alignment and geometric correction.

[0012] As a preferred solution of the coating thickness detection method based on visual monitoring of the present invention, wherein: the preliminary prediction of the coating thickness by geometric optics is carried out, and the specific steps are as follows:

[0013] Select the reflection method as the calculation method, use the optical reflection coefficient of the coating and the angle of the incident light to estimate the thickness, receive the reflected light intensity on the coating surface, measure the ratio of the reflected light intensity to the incident light intensity, and obtain the reflectivity;

[0014] Based on the principle of geometric optics, the coating thickness is preliminarily predicted using the expression:

[0015]

[0016] Where H is the preliminary predicted coating thickness, λ is the wavelength of the light wave, and R is the reflectivity.

[0017] As a preferred embodiment of the coating thickness detection method based on visual monitoring of the present invention, the coating thickness prediction is performed based on the preprocessed image and the preliminary prediction value through a multi-channel neural network. The specific steps are as follows:

[0018] Based on the multi-channel neural network, the multi-channel neural network structure is divided into two independent channels. Channel 1 extracts the spatial features of the coating surface image through a deep convolutional neural network after preprocessing, and then forms the feature vector of the image channel by splicing.

[0019] The second channel inputs the preliminary prediction value into the multi-layer perceptron, performs nonlinear mapping through the fully connected layer, and generates the feature vector of the preliminary prediction value;

[0020] The feature vector of the image channel and the feature vector of the preliminary prediction value are weightedly combined through the weighted fusion layer to obtain the final fused feature vector. The final fused feature vector is used to perform the final coating thickness prediction through the fully connected layer to generate the predicted coating thickness.

[0021] As a preferred embodiment of the coating thickness detection method based on visual monitoring of the present invention, the method includes: performing defect identification and evaluation based on the predicted coating thickness to generate the final coating thickness and defect evaluation results, and the specific steps are as follows:

[0022] Extract edge features of the pre-processed coating surface image, detect circular and linear defects on the coating surface, and perform coating surface segmentation and defect recognition in combination with morphological operations;

[0023] The area and shape characteristics of the coating surface defect area are obtained through image analysis algorithms, and the complexity score of the coating surface defect is calculated based on the shape characteristics;

[0024] The relative position impact of each defect area is defined by calculating the distance between the defect area and the coating edge. The coating surface defect severity score is calculated based on the comprehensive score of the defect area, the complexity score of the defect, and the relative position impact score. The expression is:

[0025]

[0026] Among them, E is the severity score of the coating surface defects, A n is the area of ​​the nth coating surface defect area, A max is the maximum area of ​​the coating surface, C s is the complexity score of coating surface defects, s indicates that the complexity score of coating surface defects is related to the complexity of the geometric shape of the defects, C l is the relative position impact score, where l indicates that the relative position impact score is related to the position relationship of the defect relative to the coating edge;

[0027] Based on the preliminary thickness predicted by geometric optics, the coating thickness predicted by the multi-channel neural network, and the coating surface defect severity score, the coating thickness is adjusted by a nonlinear function. Combined with environmental factors, the final adjusted coating thickness is calculated. The expression is:

[0028]

[0029] Among them, H fis the final adjusted coating thickness, P is the coating thickness predicted by the multi-channel neural network, ΔH is the thickness error feedback, γ is the error correction coefficient, and α is the environmental correction coefficient;

[0030] Based on the final adjusted coating thickness and the standard coating thickness, a coating quality assessment result is generated.

[0031] As a preferred solution of the coating thickness detection method based on visual monitoring of the present invention, wherein: the comprehensive evaluation results of defects are used to provide real-time feedback, and the specific steps are as follows:

[0032] Based on the comprehensive defect assessment results, thresholds are set based on historical data. When the defect density and size are greater than the threshold, the coating quality is deemed unqualified. A real-time alarm is sent to the operator, and the coating thickness and defect score are recorded in real time.

[0033] In a second aspect, the present invention provides a coating thickness detection system based on visual monitoring, comprising an image acquisition and preprocessing module, a coating thickness preliminary prediction module, a coating thickness prediction module, a coating thickness and defect comprehensive evaluation module, and a real-time feedback module.

[0034] The image acquisition and preprocessing module acquires coating surface images in real time and preprocesses the acquired coating surface images; the coating thickness preliminary prediction module makes a preliminary prediction of the coating thickness through geometric optics; the coating thickness prediction module predicts the coating thickness based on the preprocessed image and preliminary prediction value through a multi-channel neural network; the coating thickness and defect comprehensive evaluation module identifies and evaluates defects based on the predicted coating thickness and generates the final coating thickness and defect comprehensive evaluation results; the real-time feedback module provides real-time feedback based on the defect comprehensive evaluation results.

[0035] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the coating thickness detection method based on visual monitoring as described in the first aspect of the present invention is implemented.

[0036] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the coating thickness detection method based on visual monitoring as described in the first aspect of the present invention is implemented.

[0037] The beneficial effects of the present invention are as follows: the present invention collects coating surface images in real time and pre-processes them, combines them with geometric optics to make preliminary thickness predictions, and then uses a multi-channel neural network to make accurate coating thickness predictions, significantly improving the accuracy and adaptability of the predictions. Furthermore, through the defect recognition and evaluation module, the type, shape, complexity, and location of coating surface defects are comprehensively considered to accurately identify and quantitatively evaluate coating defects, thereby improving the accuracy and robustness of defect detection. Finally, based on the comprehensive evaluation results, real-time feedback is provided, operators are notified in a timely manner, and relevant data is recorded to ensure that the coating quality is effectively controlled, optimize quality management in the production process, and have broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 Flowchart of the coating thickness detection method based on visual monitoring in Example 1.

[0040] Figure 2 Schematic diagram of the coating thickness detection system based on visual monitoring in Example 1. DETAILED DESCRIPTION

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0043] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0044] Example 1, with reference to Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a coating thickness detection method based on visual monitoring, comprising the following steps:

[0045] S1: real-time acquisition of coating surface images and preprocessing of the acquired coating surface images;

[0046] Furthermore, a high-resolution camera is used to capture images of the coating surface in real time;

[0047] It should be noted that high-resolution cameras should be selected according to the characteristics of the coating and the application environment, and the light source should be selected according to the coating material, surface gloss and environmental conditions of the acquisition.

[0048] Perform denoising, image enhancement, illumination equalization, image segmentation, image alignment and geometric correction on the collected images of the coating surface;

[0049] It should be noted that noise is removed by an image denoising algorithm, image enhancement is performed by histogram equalization and edge enhancement, illumination equalization is performed by a Retinex algorithm, image segmentation is performed by an image segmentation algorithm, and image alignment and geometric correction are performed by a geometric correction algorithm.

[0050] S2: Preliminary prediction of coating thickness using geometric optics;

[0051] Furthermore, the reflection method is selected as the calculation method. The optical reflection coefficient of the coating and the angle of the incident light are used to estimate the thickness. The reflected light intensity is received on the coating surface, and the ratio of the reflected light intensity to the incident light intensity is measured to obtain the reflectivity. The expression is:

[0052]

[0053] Where R is the reflectivity, I r is the reflected light intensity, I0 is the incident light intensity;

[0054] Based on the principle of geometric optics, the coating thickness is preliminarily predicted using the expression:

[0055]

[0056] Where H is the preliminary predicted coating thickness and λ is the wavelength of the light wave.

[0057] It should be noted that, according to the principles of geometric optics, it is assumed that the coating material is transparent and has a known refractive index, and the reflection and refraction behavior of the coating thickness at a specific wavelength can be described by the Fresnel formula. Considering the relationship between the thickness and the reflectivity of the coating, the interference effect is introduced to obtain a preliminary predicted coating thickness expression. According to the actual measured reflectivity and incident angle, the preliminary predicted coating thickness expression is used to predict the coating thickness.

[0058] S3: Based on the preprocessed image and preliminary prediction value, the coating thickness is predicted through a multi-channel neural network;

[0059] Furthermore, based on the multi-channel neural network, the multi-channel neural network structure is divided into two independent channels. Channel one is to extract the spatial features of the coating surface image after preprocessing through a deep convolutional neural network, and then form the feature vector of the image channel by splicing;

[0060] It should be noted that the preprocessed image is extracted through multi-scale convolution features, and the expression is:

[0061]

[0062] Among them, Y l is the convolution feature map, represents the Kth convolution kernel in the lth layer, * represents the convolution operation, K represents the number of multi-scale convolution kernels, and b is the activation function;

[0063] Global average pooling is performed on the last layer of the convolutional feature map to reduce the feature dimension and retain global information;

[0064] The second channel inputs the preliminary prediction value into the multi-layer perceptron, performs nonlinear mapping through the fully connected layer, and generates the feature vector of the preliminary prediction value;

[0065] Based on the preliminary predicted thickness, a multilayer perceptron is used to enhance the features of the preliminary predicted value. The expression is:

[0066] H (l) =f(U (l) H (l-1) +v (l) );

[0067] Among them, H (l) is the output of layer l, U (l) is the bias of the lth layer, H (l-1) is the input of the l-1 layer, v (l) is the activation function;

[0068] The feature vector of the image channel and the feature vector of the preliminary prediction value are weightedly combined through the weighted fusion layer to obtain the final fused feature vector. The final fused feature vector is used to perform the final coating thickness prediction through the fully connected layer to generate the predicted coating thickness.

[0069] It should be noted that the multilayer perceptron consists of three fully connected layers and one output layer;

[0070] The feature vector of the image channel and the feature vector of the preliminary prediction value are weightedly combined through the weighted fusion layer to obtain the final fused feature vector. The final fused feature vector is used to perform the final coating thickness prediction through the fully connected layer to generate the predicted coating thickness.

[0071] S4: Based on the predicted coating thickness, defects are identified and evaluated to generate the final comprehensive evaluation results of coating thickness and defects;

[0072] Furthermore, the edge features of the pre-processed coating surface image are extracted using the Sobel operator, and the circular and linear defects on the coating surface are detected using the Hough transform. Based on the extracted edge features, the coating surface is segmented and defect recognition is performed in combination with morphological operations.

[0073] The area and shape characteristics of the coating surface defect area are obtained through image analysis algorithm. The complexity score of the coating surface defect is calculated based on the shape characteristics. The relative position influence of each defect area is defined by calculating the distance between the defect area and the coating edge. Based on the comprehensive score of the area of ​​the coating surface defect area, the complexity score of the defect and the relative position influence score, the coating surface defect severity score is calculated. The expression is:

[0074]

[0075] Among them, E is the severity score of the coating surface defects, A n is the area of ​​the nth coating surface defect area, A max is the maximum area of ​​the coating surface, C s is the complexity score of coating surface defects, s indicates that the complexity score of coating surface defects is related to the complexity of the geometric shape of the defects, C l is the relative position impact score, where l indicates that the relative position impact score is related to the position relationship of the defect relative to the coating edge;

[0076] Based on the preliminary thickness predicted by geometric optics, the coating thickness predicted by the multi-channel neural network, and the coating surface defect severity score, the coating thickness is adjusted by a nonlinear function. Combined with environmental factors, the final adjusted coating thickness is calculated. The expression is:

[0077]

[0078] Among them, H f is the final adjusted coating thickness, P is the coating thickness predicted by the multi-channel neural network, ΔH is the thickness error feedback, γ is the error correction coefficient, α is the environmental correction coefficient, and F is the normalization parameter of environmental influence;

[0079] It should be noted that the thickness error feedback ΔH=H a -H s , H a The intermediate adjusted thickness H is generated by combining the predicted coating thickness with the preliminary predicted coating thickness through several averages. a , H s is the standard thickness of the coating;

[0080] The coating quality assessment result is generated based on the final adjusted coating thickness and the standard coating thickness. It should be noted that the standard coating thickness is an important benchmark value for measuring whether the coating quality is qualified, and different fields usually have clear industry standards or specifications for coating thickness.

[0081] It should be noted that the coating quality assessment result is the ratio of the final adjusted coating thickness to the standard coating thickness. When the ratio is equal to 1, it means that the coating thickness and quality fully meet the standards. When the ratio is less than 1, it means that the coating thickness is lower than the standard, there are defects, and the quality is unqualified. When the ratio is greater than 1, it means that the coating thickness is higher than the standard, the coating is too thick, and the quality is unqualified.

[0082] S5: Provide real-time feedback based on comprehensive defect assessment results.

[0083] Furthermore, based on the comprehensive defect assessment results, when quality failure occurs, operators are notified through real-time alarms, and the coating thickness and defect score are recorded in real time.

[0084] This embodiment also provides a coating thickness detection system based on visual monitoring, comprising:

[0085] Image acquisition and preprocessing module, which acquires coating surface images in real time and preprocesses the acquired coating surface images;

[0086] Coating thickness preliminary prediction module, which makes a preliminary prediction of coating thickness through geometric optics;

[0087] The coating thickness prediction module predicts the coating thickness based on the preprocessed image and preliminary prediction value through a multi-channel neural network;

[0088] The coating thickness and defect comprehensive evaluation module identifies and evaluates defects based on the predicted coating thickness, generating the final coating thickness and defect comprehensive evaluation results;

[0089] The real-time feedback module provides real-time feedback based on the comprehensive defect evaluation results.

[0090] This embodiment also provides a computer device, which is suitable for the case of a coating thickness detection method based on visual monitoring, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the coating thickness detection method based on visual monitoring proposed in the above embodiment.

[0091] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0092] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the coating thickness detection method based on visual monitoring as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0093] In summary, the present invention significantly improves the accuracy and adaptability of the prediction by: real-time acquisition and preprocessing of coating surface images, combining geometric optics for preliminary thickness prediction, and then accurately predicting the coating thickness through a multi-channel neural network. Furthermore, through the defect recognition and evaluation module, the type, shape, complexity, and location of coating surface defects are comprehensively considered to accurately identify and quantitatively evaluate coating defects, thereby improving the accuracy and robustness of defect detection. Finally, based on the comprehensive evaluation results, real-time feedback is provided, operators are notified in a timely manner, and relevant data is recorded to ensure that the coating quality is effectively controlled, optimize quality management in the production process, and have broad application prospects.

[0094] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A coating thickness detection method based on visual monitoring, characterized in that: include, Collect coating surface images in real time and pre-process the collected coating surface images; The coating thickness is preliminarily predicted by geometric optics. The specific steps are as follows: Select the reflection method as the calculation method, use the optical reflection coefficient of the coating and the angle of the incident light to estimate the thickness, receive the reflected light intensity on the coating surface, measure the ratio of the reflected light intensity to the incident light intensity, and obtain the reflectivity; Based on the principle of geometric optics, the coating thickness is preliminarily predicted using the expression: ； Where H is the preliminary estimated coating thickness, λ is the wavelength of the light wave, and R is the reflectivity; Based on the pre-processed coating surface image and preliminary prediction value, the coating thickness is predicted through a multi-channel neural network; Based on the predicted coating thickness, defects are identified and evaluated to generate the final comprehensive evaluation results of coating thickness and defects; Provide real-time feedback through comprehensive defect assessment results.

2. The coating thickness detection method based on visual monitoring according to claim 1, characterized in that: The specific steps of real-time acquisition of coating surface images are as follows: The coating surface image is captured in real time using a high-resolution camera.

3. The coating thickness detection method based on visual monitoring according to claim 2, characterized in that: The collected coating surface image is preprocessed in the following specific steps: The collected images of the coating surface are subjected to denoising, image enhancement, illumination equalization, image segmentation, image alignment and geometric correction.

4. The coating thickness detection method based on visual monitoring according to claim 3, characterized in that: The coating thickness prediction is performed based on the pre-processed image and the preliminary prediction value through a multi-channel neural network. The specific steps are as follows: Based on the multi-channel neural network, the multi-channel neural network structure is divided into two independent channels. Channel 1 extracts the spatial features of the coating surface image through a deep convolutional neural network after preprocessing, and then forms the feature vector of the image channel by splicing. The second channel inputs the preliminary prediction value into the multi-layer perceptron, performs nonlinear mapping through the fully connected layer, and generates the feature vector of the preliminary prediction value; The feature vector of the image channel and the feature vector of the preliminary prediction value are weightedly combined through the weighted fusion layer to obtain the final fused feature vector. The final fused feature vector is used to perform the final coating thickness prediction through the fully connected layer to generate the predicted coating thickness.

5. The coating thickness detection method based on visual monitoring according to claim 4, characterized in that: The method of performing defect identification and evaluation based on the predicted coating thickness to generate the final coating thickness and defect evaluation results is as follows: Extract edge features of the pre-processed coating surface image, detect circular and linear defects on the coating surface, and perform coating surface segmentation and defect recognition in combination with morphological operations; The area and shape characteristics of the coating surface defect area are obtained through image analysis algorithms, and the complexity score of the coating surface defect is calculated based on the shape characteristics; The relative position impact of each defect area is defined by calculating the distance between the defect area and the coating edge. The coating surface defect severity score is calculated based on the comprehensive score of the defect area, the complexity score of the defect, and the relative position impact score. The expression is: ； Among them, E is the severity score of the coating surface defects, A n is the area of ​​the nth coating surface defect area, A max is the maximum area of ​​the coating surface, C s is the complexity score of coating surface defects, s indicates that the complexity score of coating surface defects is related to the complexity of the geometric shape of the defects, C l is the relative position impact score, where l indicates that the relative position impact score is related to the position relationship of the defect relative to the coating edge; Based on the preliminary thickness predicted by geometric optics, the coating thickness predicted by the multi-channel neural network, and the coating surface defect severity score, the coating thickness is adjusted by a nonlinear function, and the final adjusted coating thickness is calculated as follows: ； Among them, H f is the final adjusted coating thickness, P is the coating thickness predicted by the multi-channel neural network, ΔH is the thickness error feedback, γ is the error correction coefficient, and α is the environmental correction coefficient; Based on the final adjusted coating thickness and the standard coating thickness, a coating quality assessment result is generated.

6. The method for detecting coating thickness based on visual monitoring according to claim 5, wherein: The above-mentioned comprehensive defect evaluation results are used to provide real-time feedback. The specific steps are as follows: Based on the comprehensive defect assessment results, thresholds are set based on historical data. When the defect density and size are greater than the threshold, the coating quality is deemed unqualified. A real-time alarm is sent to the operator, and the coating thickness and defect score are recorded in real time.

7. A coating thickness detection system based on visual monitoring, based on the coating thickness detection method based on visual monitoring according to any one of claims 1 to 6, characterized in that: Including image acquisition and preprocessing module, coating thickness preliminary prediction module, coating thickness prediction module, coating thickness and defect comprehensive evaluation module and real-time feedback module, Image acquisition and preprocessing module, which acquires coating surface images in real time and preprocesses the acquired coating surface images; Coating thickness preliminary prediction module, which makes a preliminary prediction of coating thickness through geometric optics; The coating thickness prediction module predicts the coating thickness based on the preprocessed image and preliminary prediction value through a multi-channel neural network; The coating thickness and defect comprehensive evaluation module identifies and evaluates defects based on the predicted coating thickness, generating the final coating thickness and defect comprehensive evaluation results; The real-time feedback module provides real-time feedback based on the comprehensive defect evaluation results.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the coating thickness detection method based on visual monitoring according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the coating thickness detection method based on visual monitoring according to any one of claims 1 to 6 are implemented.

Images