A multispectral multi-light-intensity tire defect detection device and residual value evaluation method

Abstract

The application discloses a multispectral and multi-light-intensity tire defect detection device and a residual value evaluation method. The device comprises a rotating driving component arranged on a detection table, which is used for supporting a tire on a target rotating plane and driving the tire to rotate around a central axis of the tire; an industrial camera arranged on the detection table and adjustable in position, which is used for collecting images of the tire during rotation of the tire; a cyclically gradually changing spectrum element and a cyclically gradually changing light intensity element fixed in front of the camera through a clamping device, which are respectively used for obtaining a light source with multiple different spectra and a light source with cyclically gradually changing light intensity. The application can experimentally detect tire defects in a multispectral and multi-light-intensity manner, so that the detection rate and accuracy of tire defects can be significantly improved.

Description

Technical Field

[0001] This invention belongs to the field of tire non-destructive testing and quality assessment technology, specifically relating to a multispectral, multi-intensity tire defect detection device and residual value assessment method. Background Technology

[0002] As a critical automotive component, the health of tires directly affects driving safety. Accurate and efficient defect detection is paramount for the regular inspection of in-use tires and the remanufacturing and high-value recycling of retired tires.

[0003] Currently, common tire inspection methods mainly rely on manual visual inspection or automated inspection based on a single-spectrum and light-intensity light source. Manual inspection is inefficient, highly subjective, and prone to missing minute cracks or internal damage. Automated inspection based on a single-spectrum and light-intensity light source, such as using only a white light source with a fixed intensity, has significant limitations: a single-spectrum and light-intensity light source cannot ensure the contrast required by a visual recognition system in the complex and diverse environment of tire defects. For example, sometimes a yellow light source has high contrast and can detect tire defects that a white light source cannot detect. Too strong or too weak a light intensity may cause tire defects to be missed.

[0004] Therefore, existing technologies cannot simultaneously and accurately identify and quantify multiple types of defects on the tire surface (such as deformation defects and texture defects). This leads to uncertainty in tire residual value assessment, severely restricting the safety assurance of in-use tires and the accurate grading and high-value recycling process of retired tires. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide an automated, high-precision, and comprehensive tire defect detection device and a corresponding residual value evaluation method, so as to enable multispectral and multi-intensity detection of tire defects, thereby significantly improving the detection rate and accuracy of tire defects.

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: The present invention provides a multispectral, multi-intensity tire defect detection device, which includes: a detection stage, a rotary drive component, an industrial camera, a cyclic gradient spectral element, a cyclic gradient intensity element, and a processor. A rotary drive component is installed on the testing platform to clamp the tire and drive it to rotate at a constant speed around its own central axis. An industrial camera is mounted on the inspection table above the tire side via an adjustable bracket and aligned with the tire surface; the optical path of the industrial camera is perpendicular to the tire's plane of rotation. The cyclic gradient spectral element is fixed in front of the industrial camera lens by a clamping device. An annular spectral band is provided inside the cyclic gradient spectral element, and multiple filters of different wavelengths are embedded on the annular spectral band. The different filters of the annular spectral band are driven by a driving mechanism to rotate periodically. The cyclic gradient spectral element is installed in conjunction with the cyclic gradient light intensity element, and the driving current of the cyclic gradient light intensity element is adjusted by the pulse width modulation unit in the processor, so that the cyclic gradient light intensity element provides the industrial camera with a light source with cyclic gradient light intensity. The cyclic gradient spectral element and the cyclic gradient intensity element are coaxially aligned with the tire surface and move synchronously together with the position adjustment of the industrial camera. This allows the industrial camera to acquire a series of tire images under different spectra and intensities using the cyclic gradient spectral element and the cyclic gradient intensity element, and send them to the image processing module in the processor. The module is used to extract different types of edge intensities from the tire images and select the maximum edge intensity as the optimal edge intensity to determine tire defects.

[0007] The tire defect detection device of the present invention is also characterized in that the control unit in the processor synchronously coordinates the industrial camera, the cyclic graded spectral element, and the cyclic graded light intensity element at a tire rotation angular velocity of... Furthermore, by continuously collecting data from the tire under conditions where the wavelengths of light range from ultraviolet to visible light using a filter built into the annular spectral band, a series of incident wavelength sequences are obtained. { The rotation angle sequence is The light intensity sequence is { A sequence of tire images, wherein, Indicates the first At that moment The incident wavelength is below. Indicates the first At that moment The rotation angle below, and , The angle sampling interval is... Indicates the first At that moment The light intensity is M, where M represents the sampling time.

[0008] Furthermore, the coordinates and positional relationships of the industrial camera, the cyclic graded spectral element, and the cyclic graded intensity element are determined according to the following process: The optical center c of the industrial camera, the optical center of the cyclic gradient spectral element, and the illumination center of the cyclic gradient light intensity element are made coaxial, and the axis is denoted as the x-axis; the direction of the x-axis pointing to the optical center c is taken as the positive direction of the x-axis, and the rotation center axis of the tire is taken as the y-axis. The intersection of the x-axis and the y-axis is denoted as the origin o, thus constructing the tire coordinate system. Let the coordinates of any point P on the tire surface in the tire coordinate system be ( ρ cos θ P ,y P ),in, ρ Where is the radius of the tire. θ P Let y be the rotation angle of point P. P Let P be the y-coordinate of point P; Ling Guangxin Located in the positive direction of the x-axis, and , where d represents the working distance of the industrial camera.

[0009] Furthermore, the incident wavelength of the cyclic gradient spectral element is obtained using equation (1). : (1) In equation (1), It is the shortest wavelength of ultraviolet light. The wavelength range of ultraviolet and visible light. It is the sequence number of the spectral variation period. Let i be the i-th acquisition time of the industrial camera. The period of spectral variation for a cyclically graded spectral element; Using equation (2), the cyclic gradient light intensity element is obtained in the first... At that moment Light intensity : (2) In equation (2), As the reference light intensity, The amplitude of the light intensity change. This is the initial phase of the change in light intensity. L represents the light intensity variation period of the cyclic gradient light intensity element, and L is the offset factor.

[0010] Furthermore, tire defects are determined using the following steps: Step 1. Let The u-th incident wavelength is denoted as ,make Let the intensity of any v-th light be denoted as And obtain the u-th incident wavelength. And the monochromatic single-intensity channel matrix A under the v-th light intensity uv Thus, M×M multispectral multi-intensity channel matrices [A] are obtained. uv |u∈(1,M), v∈(1,V)]; Using the maximum value method to [A] uv |u∈(1,M),v∈(1,V)] are transformed into the maximum grayscale image matrix B; Using the minimum value method to [A] uv |u∈(1,M),v∈(1,V)] is transformed into the minimum grayscale image matrix C; Using the average value method to [A] uv |u∈(1,M),v∈(1,V)] are transformed into the average grayscale image matrix F; Using the median method to [A] uv |u∈(1,M), v∈(1,V)] are transformed into the median grayscale image matrix H; Step 2. Multiply the m×m neighborhood centered on each pixel in the grayscale image matrix B element-wise with both the horizontal and vertical convolution kernels, and then sum the results to obtain the horizontal gradient. and vertical gradient Therefore, based on and The edge strength of B is obtained using the Euclidean norm. Similarly, calculate the edge strength of C. Edge strength of F Edge strength of H and from The maximum value is selected as the optimal edge strength of the tire. .

[0011] Step 3: When the tire edge strength > Tire defect detection rate > At that time, Areas exceeding a set threshold S are identified as defect areas. Each defect within these areas is evaluated based on its morphology, texture, and edge distribution characteristics to determine its severity. The edge strength of the tire under composite white light. For edge intensity under multispectral and multi-intensity detection, The edge intensity is measured under composite white light.

[0012] The residual value evaluation method of the tire defect detection device of the present invention is characterized by including the following steps: S1, using formula (3) tire baseline residual value : (3) In equation (3), Indicates the highest potential use value. This represents the processing costs, including: sorting costs, transportation costs, and processing costs; S2: Calculate the tire residual value using equation (4) : (4) In equation (4), The severity of the tire's maximum defect. This indicates the severity of the j-th defect. This is the tire's baseline residual value. Let N represent the weight coefficient of the j-th defect, and N represent the total number of defects in the defect region.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Multispectral light source: By rotating the cyclically gradient spectral element, the annular spectral band in the cavity is periodically aligned with the camera, thereby filtering and separating the commonly used white (composite light) into multiple light sources with different spectra. This overcomes the limitations of a single spectral light source and can complementarily detect complex and diverse defects (such as cracks, missing parts, and perforations), greatly improving the comprehensiveness and accuracy of the detection.

[0014] 2. Multiple light sources: The core principle of the cyclic gradient light intensity element is pulse width modulation dimming technology. The control unit generates a periodically changing control signal, which controls the current flowing through the light source through the drive circuit, thereby causing the light intensity to change synchronously and smoothly in a periodic manner. This overcomes the limitations of a single light source and can complementarily detect complex and diverse defects (such as cracks, missing parts, and perforations), greatly improving the comprehensiveness and accuracy of the detection.

[0015] 3. High tire defect detection rate: The maximum edge strength of the tire is obtained by comprehensively considering the maximum value method, minimum value method, average value method, and median method. Therefore, it can be inferred that multispectral multi-intensity detection is superior to ordinary composite white light detection.

[0016] 4. The residual value assessment method is simple and efficient: for Areas exceeding a set threshold S are identified as defective areas. Defects are classified based on their shape, texture, and edge distribution characteristics, and their severity is assessed. Based on the quantification results of tire defect type, location, and severity, combined with the tire's highest potential use value and processing cost, and considering the company's actual needs, a weighting coefficient for each tire defect is given, thereby efficiently quantifying the tire's residual value.

[0017] 5. High degree of automation and significantly improved detection efficiency: The entire process from tire clamping, synchronous scanning, image acquisition to defect analysis and judgment is automated, replacing inefficient and subjective manual visual inspection, and is suitable for rapid detection scenarios of large-scale tires.

[0018] 6. More comprehensive and accurate identification and detection, enabling green remanufacturing and utilization: This invention achieves high-accuracy identification and detection of defects, providing a solid and reliable data foundation for the safety status assessment and life prediction of in-use tires, as well as the accurate classification of retired tires, the judgment of remanufacturing feasibility, and the planning of high-value recycling paths, directly promoting the low-carbon and intelligent efficient recycling of tires. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall structure of the multispectral multi-intensity tire defect detection device of the present invention; Figure 2 This is a schematic diagram of the coordinate system of the multispectral multi-intensity tire defect detection device in this invention; Figure 3 This is a schematic diagram illustrating the principle of the tire's highest potential use value in this invention; Figure 4 This is a logic block diagram of the multispectral multi-intensity tire defect detection device and residual value evaluation method in this invention. Detailed Implementation

[0020] In this embodiment, a multispectral, multi-intensity tire defect detection device, such as... Figure 1 As shown, it includes: a detection stage 1, a rotary drive component 2, an industrial camera 3, a cyclic gradient spectral element 4, a cyclic gradient light intensity element 5, and a processor; A rotary drive component 2 is installed on the testing table 1 to clamp the tire and drive it to rotate at a constant speed around its own central axis; An industrial camera 3 is mounted on the inspection table 1, above the tire side, via an adjustable bracket, and is aimed at the tire surface; the optical path of the industrial camera 3 is perpendicular to the tire's plane of rotation. A cyclic gradient spectral element 4 is fixed in front of the lens of the industrial camera 3 by a clamping device. An annular spectral band is provided inside the cyclic gradient spectral element 4, and multiple filters of different wavelengths are embedded on the annular spectral band. The different filters of the annular spectral band are driven by a driving mechanism to rotate periodically.

[0021] A cyclic gradient light intensity element 5 is installed in conjunction with the cyclic gradient spectral element 4, and the driving current of the cyclic gradient light intensity element 5 is adjusted by the pulse width modulation unit in the processor, so that the cyclic gradient light intensity element 5 provides a light source with cyclic gradient light intensity for the industrial camera 3. The cyclically graded light intensity element 5, in conjunction with the cyclically graded spectral element 4, smoothly and periodically changes the pulse width modulation duty cycle through program control to achieve multispectral and multi-intensity identification. Furthermore, when the intensity variation period is an irrational multiple of the spectral variation period, all intensities can be traversed for any given spectrum.

[0022] The cyclic gradient spectral element 4 and the cyclic gradient intensity element 5 are coaxially aligned with the tire surface and move synchronously together with the position adjustment of the industrial camera 3. In specific implementation, such as... Figure 2 As shown, the coordinates and positional relationships of the industrial camera 3, the cyclic graded spectral element 4, and the cyclic graded intensity element 5 are determined according to the following process: Make the optical center c of the industrial camera 3, the optical center of the cyclic gradient spectral element 4, and the illumination center of the cyclic gradient light intensity element 5 coaxial, and denote the axis as the x-axis; and take the direction of the x-axis pointing to the optical center c as the positive direction of the x-axis, and take the rotation center axis of the tire as the y-axis, and denote the intersection of the x-axis and the y-axis as the origin o, thereby constructing the tire coordinate system.

[0023] Let the coordinates of any point P on the tire surface in the tire coordinate system be... ρ cos θ P ,y P ,in, ρ Where is the radius of the tire. θ P Let y be the rotation angle of point P. P Let P be the y-coordinate of point P; Ling Guangxin Located in the positive direction of the x-axis, and , where d represents the working distance of the industrial camera (3).

[0024] The processor coordinates the industrial camera 3, the cyclic graded spectral element 4, and the cyclic graded intensity element 5 at a tire rotational angular velocity of Furthermore, under conditions where the wavelengths of light from ultraviolet to visible light are filtered within a ring-shaped spectral band, continuous data is collected from the tire to obtain a series of incident wavelength sequences under different spectra and light intensities. { The rotation angle sequence is The light intensity sequence is { A sequence of tire images, wherein, Indicates the first At that moment The incident wavelength is below. Indicates the first At that moment The rotation angle below, and , The angle sampling interval is... Indicates the first At that moment The light intensity is M, where M represents the sampling time.

[0025] In specific implementation, the incident wavelength of the cyclic gradient spectral element (4) is obtained using equation (1). : (1) In equation (1), It is the shortest wavelength of ultraviolet light. The wavelength range of ultraviolet and visible light. It is the sequence number of the spectral variation period. Let i be the i-th acquisition time of the industrial camera (3). The spectral variation period of the cyclic gradient spectral element (4) is given.

[0026] Using equation (2), the cyclic gradient light intensity element (5) is obtained in the first... At that moment Light intensity : (2) In equation (2), As the reference light intensity, The amplitude of the light intensity change. This is the initial phase of the change in light intensity. L is the light intensity variation period of the cyclic gradient light intensity element (5), and L is the offset factor.

[0027] In this embodiment, the image processing module in the processor extracts different types of edge intensities from a series of tire images under different spectra and light intensities according to the following steps, and selects the maximum edge intensity as the optimal edge intensity to evaluate the severity of each defect: Step 1. Let The u-th incident wavelength is denoted as ,make Let the intensity of any v-th light be denoted as And obtain the u-th incident wavelength. And the monochromatic single-intensity channel matrix A under the v-th light intensity uv Thus, M×M multispectral multi-intensity channel matrices [A] are obtained. uv |u∈(1,M), v∈(1,M)]; Let the element at any fixed position in the monochromatic single-intensity channel matrix be a. uv Then we obtain the elements of M×M multispectral multi-intensity channel matrices [a uv |u∈(1,M), v∈(1,M)].

[0028] Using the maximum value method shown in equation (5), the element at each fixed position [a uv |u∈(1,M), v∈(1,M)] are converted to the maximum gray value Thus, [A] uv |u∈(1,M), v∈(1,M)] are transformed into the maximum grayscale image matrix B; (5) Using the minimum value method shown in equation (6), the element at each fixed position [a uv |u∈(1,M), v∈(1,M)] are transformed into the minimum gray value Thus, [A] uv |u∈(1,M), v∈(1,M)] are transformed into the maximum grayscale image matrix C; (6) Using the average value method shown in equation (7), the element at each fixed position [a] is... uv |u∈(1,M), v∈(1,M)] are converted to average gray values. Thus, [A] uv |u∈(1,M), v∈(1,M)] are transformed into the maximum grayscale image matrix F; (7) Median method: The multispectral multi-intensity channel matrix A... uv Convert to a grayscale image matrix H, first sort the elements at each fixed position in ascending order [a...]. uv Sort the values ​​of u∈(1,M) and v∈(1,M) and take the median (or the average of the two middle values ​​if there are an even number of values) as the median gray value. Thus, [A] uv |u∈(1,M), v∈(1,M)] are transformed into the median grayscale image matrix H; All of the above boundary issues are handled using the zero-fill method (filling the image with a ring of 0-value pixels).

[0029] Step 2. Convolution operation: Multiply the m×m neighborhood centered on each pixel in the grayscale image matrix B element-wise with both the horizontal and vertical convolution kernels, and then sum the results to obtain the horizontal gradient. and vertical gradient Then, based on the Euclidean norm, its edge strength is calculated using equation (8). : (8) The horizontal gradient is obtained by summing the element-wise multiplications of the m×m neighborhood centered on each pixel in the grayscale image matrix C with both the horizontal and vertical convolution kernels. and vertical gradient Then, based on the Euclidean norm, its edge strength is calculated using equation (9). : (9) The horizontal gradient is obtained by summing the element-wise multiplications of the m×m neighborhood centered on each pixel in the grayscale image matrix F with both the horizontal and vertical convolution kernels. and vertical gradient Then, based on the Euclidean norm, its edge strength is calculated using equation (10). : (10) grayscale image matrix The horizontal gradient is obtained by summing the element-wise multiplications of the m×m neighborhood centered on each pixel with both the horizontal and vertical convolution kernels. and vertical gradient Then, based on the Euclidean norm, its edge strength is calculated using equation (11). : (11) Calculate the maximum edge strength of the tire Edge strength can quantify the degree of pixel abrupt changes in a tire image; the greater the degree of pixel abrupt changes, the higher the probability that the area represents a tire defect.

[0030] Step 3. When the tire edge strength > Tire defect detection rate > At that time, Areas exceeding a set threshold S are identified as defect areas. Each defect within a defect area is then classified based on its morphology, texture, and edge distribution characteristics, and the severity of each defect is assessed. The edge strength of the tire under composite white light. For edge intensity under multispectral and multi-intensity detection, The edge intensity is measured under composite white light.

[0031] In this embodiment, after obtaining the severity of each defect, residual evaluation is performed according to the following steps: S1, using formula (3) tire baseline residual value : (3) In equation (3), This indicates the highest potential use value, and the method for determining the highest potential use value is as follows: Figure 3 As shown, This represents the processing costs, including: sorting costs, transportation costs, and processing costs.

[0032] S2: Calculate the tire residual value using equation (4) : (4) In equation (4), The severity of the tire's maximum defect. This indicates the severity of the j-th defect. This is the tire's baseline residual value. Let N represent the weight coefficient of the j-th defect, and N represent the total number of defects in the defect region.

[0033] S3: Based on the tire residual value, make a comprehensive decision on tire recycling, such as... Figure 4 As shown, the total number and severity of defects are determined based on the tire defect detection results. The final residual value is then assessed in conjunction with the tire's benchmark residual value, and the tire disposal method that maximizes value and meets environmental protection requirements is finally selected.

Claims

1. A multispectral, multi-intensity tire defect detection device, characterized in that, include: The testing stage (1), the rotary drive component (2), the industrial camera (3), the cyclic gradient spectral element (4), the cyclic gradient light intensity element (5), and the processor; A rotary drive component (2) is installed on the testing table (1) for clamping the tire and driving it to rotate at a constant speed around its own central axis; An industrial camera (3) is mounted on the inspection table (1) above the tire side via an adjustable bracket and aligned with the tire surface; and the optical path of the industrial camera (3) is perpendicular to the rotation plane of the tire. The cyclic gradient spectral element (4) is fixed in front of the lens of the industrial camera (3) by a clamping device. An annular spectral band is provided inside the cyclic gradient spectral element (4), and multiple filters of different wavelengths are embedded on the annular spectral band. The different filters of the annular spectral band are driven by a driving mechanism to rotate periodically. The cyclic gradient spectral element (4) is equipped with the cyclic gradient light intensity element (5), and the driving current of the cyclic gradient light intensity element (5) is adjusted by the pulse width modulation unit in the processor so that the cyclic gradient light intensity element (5) provides the industrial camera (3) with a light source with cyclic gradient light intensity. The cyclic gradient spectral element (4) and the cyclic gradient light intensity element (5) are coaxially aligned with the tire surface and move synchronously together with the position adjustment of the industrial camera (3). Thus, the industrial camera (3) acquires a series of tire images under different spectra and light intensities under the action of the cyclic gradient spectral element (4) and the cyclic gradient light intensity element (5) and sends them to the image processing module in the processor to extract different types of edge intensities of the tire images and select the maximum edge intensity as the optimal edge intensity to determine tire defects.

2. The tire defect detection device according to claim 1, characterized in that, The control unit in the processor synchronously coordinates the industrial camera (3), the cyclic gradient spectral element (4), and the cyclic gradient light intensity element (5) at a tire rotation angular velocity of Furthermore, by continuously collecting data from the tire under conditions where the wavelengths of light range from ultraviolet to visible light using a filter built into the annular spectral band, a series of incident wavelength sequences are obtained. { The rotation angle sequence is The light intensity sequence is { A sequence of tire images, wherein, Indicates the first At that moment The incident wavelength is below. Indicates the first At that moment The rotation angle below, and , The angle sampling interval is... Indicates the first At that moment The light intensity is M, where M represents the sampling time.

3. The tire defect detection device according to claim 1, characterized in that, The coordinates and positional relationships of the industrial camera (3), the cyclic graded spectral element (4), and the cyclic graded intensity element (5) are determined according to the following process: Make the optical center c of the industrial camera (3), the optical center of the cyclic gradient spectral element (4), and the illumination center of the cyclic gradient light intensity element (5) coaxial, and denote the axis as the x-axis; and take the direction of the x-axis pointing to the optical center c as the positive direction of the x-axis, take the rotation center axis of the tire as the y-axis, and denote the intersection of the x-axis and the y-axis as the origin o, thereby constructing the tire coordinate system; Let the coordinates of any point P on the tire surface in the tire coordinate system be ( ρ cos θ P ,y P ),in, ρ Where is the radius of the tire. θ P Let y be the rotation angle of point P. P Let P be the y-coordinate of point P; Ling Guangxin Located in the positive direction of the x-axis, and , where d represents the working distance of the industrial camera (3).

4. The tire defect detection device according to claim 1, characterized in that, The incident wavelength of the cyclic gradient spectral element (4) is obtained using equation (1). : (1) In equation (1), It is the shortest wavelength of ultraviolet light. The wavelength range of ultraviolet and visible light. It is the sequence number of the spectral variation period. Let i be the i-th acquisition time of the industrial camera (3). The spectral variation period of the cyclic gradient spectral element (4) is used; Using equation (2), the cyclic gradient light intensity element (5) is obtained in the first... At that moment Light intensity : (2) In equation (2), As the reference light intensity, The amplitude of the light intensity change. This is the initial phase of the change in light intensity. L is the light intensity variation period of the cyclic gradient light intensity element (5), and L is the offset factor.

5. The tire defect detection device according to claim 2, characterized in that, The following steps are used to determine tire defects: Step 1. Let The u-th incident wavelength is denoted as ,make Let the intensity of any v-th light be denoted as And obtain the u-th incident wavelength. And the monochromatic single-intensity channel matrix A under the v-th light intensity uv Thus, M×M multispectral multi-intensity channel matrices [A] are obtained. uv |u∈(1,M), v∈(1,V)]; Using the maximum value method to [A] uv |u∈(1,M),v∈(1,V)] are transformed into the maximum grayscale image matrix B; Using the minimum value method to [A] uv |u∈(1,M),v∈(1,V)] is transformed into the minimum grayscale image matrix C; Using the average value method to [A] uv |u∈(1,M),v∈(1,V)] are transformed into the average grayscale image matrix F; Using the median method to [A] uv |u∈(1,M), v∈(1,V)] are transformed into the median grayscale image matrix H; Step 2. Multiply the m×m neighborhood centered on each pixel in the grayscale image matrix B element-wise with both the horizontal and vertical convolution kernels, and then sum the results to obtain the horizontal gradient. and vertical gradient Therefore, based on and The edge strength of B is obtained using the Euclidean norm. ; Similarly, calculate the edge strength of C. Edge strength of F Edge strength of H and from The maximum value is selected as the optimal edge strength of the tire. . Step 3: When the tire edge strength > Tire defect detection rate > At that time, Areas exceeding a set threshold S are identified as defect areas. Each defect within these areas is evaluated based on its morphology, texture, and edge distribution characteristics to determine its severity. The edge strength of the tire under composite white light. For edge intensity under multispectral and multi-intensity detection, The edge intensity is measured under composite white light.

6. The residual value assessment method for the tire defect detection device according to claim 5, characterized in that, Includes the following steps: S1, using formula (3) tire baseline residual value : (3) In equation (3), Indicates the highest potential use value. This represents the processing costs, including: sorting costs, transportation costs, and processing costs; S2: Calculate the tire residual value using equation (4) : (4) In equation (4), The severity of the tire's maximum defect. This indicates the severity of the j-th defect. This is the tire's baseline residual value. Let N represent the weight coefficient of the j-th defect, and N represent the total number of defects in the defect region.

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