Textile fabric key point high-precision detection method based on ultraviolet rays

Abstract

The invention discloses a textile fabric key point high-precision detection method based on ultraviolet rays, and relates to the technical field of textile materials. Comprising the following steps: determining an ultraviolet wavelength, constructing an ultraviolet fluorescence spectrum feature library, adjusting an ultraviolet light source, carrying out image acquisition on the surface of a textile fabric, preprocessing the acquired ultraviolet fluorescence image, carrying out key point feature extraction, carrying out defect identification and classification, and carrying out accurate positioning on a defect area through image segmentation. And outputting a detection result. According to the high-precision detection method for the key points of the textile fabric based on the ultraviolet rays, by introducing a spectral texture joint distribution model and a significance segmentation model, deep fusion of multi-dimensional features and accurate positioning of the key points are achieved. Under the support of uniform irradiation of the ultraviolet light source, in combination with multi-dimensional significance analysis, the detection robustness and segmentation precision under a complex background are greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of textile materials, and in particular to a high-precision detection method for key points of textiles based on ultraviolet rays. Background Art

[0002] In textile manufacturing and quality control, high-precision detection of key points is an important means to identify surface defects, cracks and other anomalies of fabrics. Traditional detection methods rely on visible light image analysis or manual visual detection, which makes it difficult to accurately identify tiny defects and internal structural changes. In recent years, ultraviolet-based detection technology has gradually become a research hotspot because it can reveal hidden features and spectral anomalies on the surface of fabrics. However, the existing technology has the following main shortcomings:

[0003] Single feature extraction: Most methods perform detection based on only one dimension of texture, spectrum or edge features, ignoring the complementarity between these features, resulting in insufficient robustness of detection in complex texture backgrounds.

[0004] Low segmentation accuracy: Traditional segmentation methods, such as algorithms based on fixed thresholds or simple edge detection, are difficult to adapt to the complex changes in fabric surface texture and spectral response, resulting in inaccurate defect positioning. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a high-precision detection method for key points of textiles based on ultraviolet rays to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a high-precision detection method for key points of a textile based on ultraviolet light, comprising the following steps:

[0008] S1. Determine the wavelength of ultraviolet light for detection based on fluorescence spectrum analysis;

[0009] S2, constructing a UV fluorescence spectrum feature library for textiles;

[0010] S3. Conduct a comprehensive analysis of the detection area and adjust the UV light source;

[0011] S4, using a high-resolution UV camera to collect images of the textile surface;

[0012] S5, preprocessing the collected ultraviolet fluorescence image;

[0013] S6, extracting key point features from the preprocessed image;

[0014] S7, identifying and classifying defects using the extracted key point features;

[0015] S8. Based on defect recognition, the defect area is accurately located through image segmentation;

[0016] S9. Output the test results and generate a test report.

[0017] To further optimize the technical solution, in step S1, the surface of the textile is irradiated by an ultraviolet light source to stimulate a fluorescent reaction on the surface of the textile, and a spectral analysis is performed to evaluate the fluorescent response characteristics of the textile after ultraviolet irradiation, and to determine the ultraviolet wavelength for detection.

[0018] To further optimize the technical solution, in step S2, ultraviolet irradiation experiments are performed on textile samples of different batches, materials and dyeing methods, and their fluorescence spectrum characteristics are recorded to form an ultraviolet fluorescence spectrum characteristic library;

[0019] The feature library contains all variables that may affect the detection accuracy, including factors such as textile material, dye type, and fabric structure.

[0020] To further optimize the technical solution, in step S3, an ultraviolet intensity distribution measuring instrument is used to detect the ultraviolet intensity distribution of the detection area;

[0021] By adjusting the position, angle and irradiation distance of the UV light source, uniform UV distribution can be achieved to ensure that each inspection point of the textile can be evenly irradiated by UV rays.

[0022] To further optimize the technical solution, in step S5, the preprocessing includes the following process:

[0023] First, an image filtering algorithm is applied to remove background noise and eliminate ambient light interference;

[0024] Then, the image enhancement algorithm is used to enhance the contrast of the image and to enhance the image in areas with weaker fluorescence, so that the tiny defects on the surface of the textile can be clearly presented.

[0025] To further optimize the technical solution, in step S6, when extracting key point features, a spectral texture joint distribution model is constructed to integrate the fluorescence response, texture change and shape characteristics of the textile surface, and coupled using weight factors;

[0026] In the spectral texture joint distribution model, any pixel point on the textile surface is assumed to be , its eigenvalue is calculated by the following formula:

[0027] ;

[0028] in,

[0029] : Pixels The comprehensive eigenvalue of is used to indicate the possibility of this point as a key point;

[0030] : The eigenvalue based on texture gradient is used to represent the texture complexity of the pixel;

[0031] : Spectral intensity characteristic value, calculated by the spectral response of the pixel point in the ultraviolet fluorescence image;

[0032] : Shape eigenvalue, calculated by the significance of the pixel in the surrounding geometric structure;

[0033] : Weight factor, indicating the importance of texture, spectrum, and shape features in key point extraction.

[0034] To further optimize the technical solution, in the spectral texture joint distribution model, the calculation of the eigenvalue includes:

[0035] Texture gradient feature calculation:

[0036] Spectral intensity feature calculation;

[0037] Shape feature calculation;

[0038] Weight optimization and feature fusion.

[0039] To further optimize the technical solution, in step S7, the extracted key point features are input into the machine learning model. Through the training set sample data, the machine learning model learns the characteristics of different types of textile defects and automatically identifies and classifies new samples.

[0040] Further optimizing the technical solution, in step S8, a saliency segmentation model is constructed, and a segmentation mask is dynamically generated by performing multi-dimensional saliency calculation on the spectral response, texture complexity and edge strength of the ultraviolet fluorescence image to achieve accurate positioning of the defect area;

[0041] Assume the input ultraviolet fluorescence image is , generating a segmentation mask , defined as:

[0042] ;

[0043] in,

[0044] : The saliency function is defined as the comprehensive saliency value of spectrum, texture and edge, which is used to evaluate whether the pixel belongs to the defect area;

[0045] : Adaptive threshold, dynamically calculated based on the global statistical properties of the saliency function;

[0046] : Output segmentation mask, where 1 represents the defect area and 0 represents the background area.

[0047] Further optimizing the technical solution, the significance function As shown below:

[0048] ;

[0049] in,

[0050] : Spectral significance, reflecting the abnormality of the textile surface in ultraviolet fluorescence response;

[0051] : Texture saliency, describing the difference between texture complexity and surroundings;

[0052] : Edge saliency, used to capture local edge structure characteristics;

[0053] : Weight factor used to balance the importance of spectral, texture and edge saliency.

[0054] In a second aspect, an embodiment of 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 instructions are executed by the processor, the steps of a high-precision detection method for key points of textiles based on ultraviolet rays are implemented as described in the first aspect of the present invention.

[0055] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a high-precision detection method for key points of textiles based on ultraviolet rays are implemented as described in the first aspect of the present invention.

[0056] Compared with the prior art, the present invention provides a high-precision detection method for key points of textiles based on ultraviolet light, which has the following beneficial effects:

[0057] This high-precision detection method for textile key points based on ultraviolet light achieves deep fusion of multi-dimensional features and precise positioning of key points by introducing a spectral texture joint distribution model and a saliency segmentation model. With the support of uniform irradiation of ultraviolet light sources and combined with multi-dimensional saliency analysis, the detection robustness and segmentation accuracy under complex backgrounds are greatly improved. This method can be adapted to textiles of various materials and processes, accurately detect tiny defects, and provide an efficient and stable solution for textile quality control. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] 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 creative work.

[0059] Figure 1 A schematic diagram of a process of a high-precision detection method for key points of textiles based on ultraviolet light proposed by the present invention;

[0060] Figure 2 This is a flow chart of the spectral-texture joint distribution model in the ultraviolet-based high-precision detection method for textile key points proposed in the present invention. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and 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.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0064] Embodiment 1:

[0065] Reference Figure 1 , which is the first embodiment of the present invention, provides a high-precision detection method for key points of textiles based on ultraviolet light, comprising the following steps:

[0066] S1. Determine the wavelength of ultraviolet light for detection based on fluorescence spectroscopy analysis

[0067] In this embodiment, at the beginning of the detection method, it is first necessary to irradiate the surface of the textile with an ultraviolet (UV) light source to stimulate the fluorescence reaction on the surface. Different textiles or fabric treatments react differently to ultraviolet light, so a spectral analysis is first performed to evaluate the fluorescence response characteristics of the textile after ultraviolet irradiation. By testing the irradiation effects of different ultraviolet bands, it can help determine the most suitable ultraviolet wavelength and find out the hidden defects or tiny cracks that may exist on the surface of the textile.

[0068] S2. Constructing a UV fluorescence spectral feature library for textiles

[0069] In this embodiment, ultraviolet irradiation experiments are performed on textile samples of different batches, materials and dyeing methods, and their fluorescence spectrum characteristics are recorded to form an ultraviolet fluorescence spectrum characteristic library;

[0070] The feature library contains all variables that may affect the detection accuracy, including factors such as textile material, dye type, and fabric structure. These data will provide a scientific basis for subsequent feature matching and key point detection, and can improve the robustness and accuracy of key point detection in actual operation.

[0071] S3. Comprehensively analyze the detection area and adjust the UV light source

[0072] In this embodiment, the intensity of ultraviolet radiation needs to ensure uniformity to avoid data deviation caused by local strong light or weak light. Use an ultraviolet intensity distribution measuring instrument to detect the ultraviolet intensity distribution in the detection area;

[0073] By adjusting the position, angle and irradiation distance of the UV light source, uniform UV distribution can be achieved to ensure that each inspection point of the textile is evenly irradiated by UV light, thereby avoiding inspection errors caused by uneven UV intensity.

[0074] S4. Use high-resolution UV camera to capture images of textile surfaces

[0075] In this embodiment, the UV camera needs to have a high frame rate and high resolution to accurately capture the tiny defects or structural changes that may exist on the surface of the textile. The UV camera should also have the function of automatically adjusting the focus and exposure time to ensure that clear and accurate image data can be obtained under different lighting conditions, reduce manual intervention, and improve the efficiency and consistency of detection.

[0076] S5. Preprocessing the collected ultraviolet fluorescence image

[0077] In this embodiment, the preprocessing includes the following steps:

[0078] First, an image filtering algorithm is applied to remove background noise and eliminate ambient light interference;

[0079] Then, the image enhancement algorithm is used to enhance the contrast of the image, especially in the area with weak fluorescence, so that the tiny defects on the surface of the textile are more obvious. This process needs to ensure that the processed image does not lose real information due to excessive enhancement, so as to ensure image quality.

[0080] S6. Extract key point features from the preprocessed image

[0081] In this embodiment, when extracting key point features, a spectrum-texture joint distribution model is constructed to integrate the fluorescence response, texture change and shape characteristics of the textile surface, and the weight factors are used to couple them into a unified feature extraction framework;

[0082] In the spectral texture joint distribution model, any pixel point on the textile surface is assumed to be , its eigenvalue is calculated by the following formula:

[0083] ;

[0084] in,

[0085] : Pixels The comprehensive eigenvalue of is used to indicate the possibility of this point as a key point;

[0086] : The eigenvalue based on texture gradient is used to represent the texture complexity of the pixel;

[0087] : Spectral intensity characteristic value, calculated by the spectral response of the pixel point in the ultraviolet fluorescence image;

[0088] : Shape eigenvalue, calculated by the significance of the pixel in the surrounding geometric structure;

[0089] : Weight factor, indicating the importance of texture, spectrum, and shape features in key point extraction.

[0090] S7. Identify and classify defects using the extracted key point features

[0091] In this embodiment, the extracted key point features are input into the machine learning model. Through the training set sample data, the machine learning model learns the characteristics of different types of textile defects and automatically identifies and classifies new samples. Commonly used machine learning algorithms such as support vector machines (SVM) and convolutional neural networks (CNN) can select appropriate algorithms according to the different needs of samples. This step can greatly improve the degree of automation of defect identification and reduce errors caused by human intervention.

[0092] S8. Based on defect recognition, the defect area is accurately located through image segmentation

[0093] In this embodiment, a saliency segmentation model is constructed, and a segmentation mask is dynamically generated by performing multi-dimensional saliency calculation on the spectral response, texture complexity and edge strength of the ultraviolet fluorescence image to achieve accurate positioning of the defect area;

[0094] Assume the input ultraviolet fluorescence image is , generating a segmentation mask , defined as:

[0095] ;

[0096] in,

[0097] : The saliency function is defined as the comprehensive saliency value of spectrum, texture and edge, which is used to evaluate whether the pixel belongs to the defect area;

[0098] : Adaptive threshold, dynamically calculated based on the global statistical properties of the saliency function;

[0099] : Output segmentation mask, where 1 represents the defect area and 0 represents the background area.

[0100] S9. Output the test results and generate a test report

[0101] In this embodiment, a detailed defect report is generated by integrating the results of each step. The report not only includes the defect location and type, but also provides corresponding detection parameters, processing steps and possible repair suggestions. This report will provide an important basis for the quality control and improvement of textiles and help relevant personnel make subsequent decisions.

[0102] Embodiment 2:

[0103] Reference Figure 2 , which is the second embodiment of the present invention, and provides a method for calculating eigenvalues ​​in a spectrum-texture joint distribution model, the method comprising:

[0104] Texture gradient feature calculation

[0105] The texture changes of the textile surface are calculated by gradient. The second-order derivative is used to calculate the complexity of the pixel point on the texture:

[0106] ;

[0107] in, represents the image intensity value, and The images are and This characteristic value can effectively distinguish areas with obvious structural changes on the textile surface, such as break points or discontinuous lines.

[0108] Spectral intensity feature calculation

[0109] The spectral intensity in the ultraviolet fluorescence image is used as the basis for determining the key points. The spectral characteristic value is defined as:

[0110] ;

[0111] in, Yes In wavelength The fluorescence intensity under . It is the effective wavelength range of ultraviolet light (selected based on experiments, such as 250-400nm).

[0112] This feature reflects differences in the properties of the textile under UV light, such as changes in the spectral response caused by dye distribution or material defects.

[0113] Shape feature calculation

[0114] Define shape eigenvalues ​​using the saliency of local geometric structure:

[0115] ;

[0116] in, Indicate point A local geometric descriptor of θ (such as the response of a Gabor filter). Yes The surrounding N neighboring pixels.

[0117] The shape feature value calculates the significance through the geometric difference with the neighboring pixels, which is suitable for detecting cracks, holes or other deformations on the textile surface.

[0118] Weight optimization and feature fusion

[0119] Initial weight It can be determined by cross-validation on the actual data set to optimize the performance of key point extraction. In addition, an adaptive mechanism can be introduced:

[0120] ;

[0121] in, It is the variance of each feature dimension and is used to adaptively adjust the weights so that the model can be dynamically adjusted according to the characteristics of the input data.

[0122] The model integrates texture, spectrum and shape features to improve the accuracy of key point extraction. The adaptive weight optimization mechanism enhances the model's adaptability to different textile materials and dye types. The introduction of shape features can effectively detect complex cracks or local structural anomalies.

[0123] This embodiment also provides a method for calculating a saliency function. The saliency function in the saliency segmentation model As shown below:

[0124] ;

[0125] in,

[0126] : Spectral significance, reflecting the abnormality of the textile surface in ultraviolet fluorescence response;

[0127] : Texture saliency, describing the difference between texture complexity and surroundings;

[0128] : Edge saliency, used to capture local edge structure characteristics;

[0129] : Weight factor used to balance the importance of spectral, texture and edge saliency.

[0130] Based on the model, significant component calculations are performed, including:

[0131] Spectral significance calculation

[0132] Detecting defects on textile surfaces using spectral anomalies:

[0133] ;

[0134] in, Yes In wavelength The fluorescence intensity under .

[0135] It is the spectral mean of the pixel in its neighborhood, indicating a normal spectral distribution.

[0136] This significance value can effectively detect spectral anomalies such as uneven dye distribution or material changes.

[0137] Texture saliency calculation

[0138] Saliency is defined by comparing texture features with the surrounding area:

[0139] ;

[0140] in, Yes The texture feature values ​​(such as calculated by Gabor filter).

[0141] It is the average texture feature value of the local neighborhood of the pixel.

[0142] Areas with high values ​​usually correspond to defective areas where the texture changes abruptly, such as break points or weave gaps.

[0143] Edge saliency calculation

[0144] Compute saliency via gradient-enhanced edge detection:

[0145] ;

[0146] Edge saliency reflects the changes in the local structure of the textile surface and helps to accurately locate boundaries such as cracks and holes.

[0147] When the model is used, it includes the following processes:

[0148] Saliency map generation: For the input fluorescence image, the spectral saliency, texture saliency, and edge saliency are calculated respectively, and then the comprehensive saliency map is calculated based on the saliency function.

[0149] Adaptive threshold segmentation: Calculate the adaptive threshold based on the statistical characteristics of the saliency map. For each pixel in the saliency map, determine whether its saliency value is greater than the threshold. If it is greater, it is classified as a defect area.

[0150] Post-processing optimization: Apply morphological operations (such as dilation and erosion) and connectivity analysis to the generated segmentation masks to eliminate noise points and fill in the breaks in small areas to obtain accurate defect segmentation results.

[0151] Embodiment three:

[0152] This embodiment also provides a computer device, which is suitable for a high-precision detection method for key points of textiles based on ultraviolet rays, and includes a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the high-precision detection method for key points of textiles based on ultraviolet rays as proposed in the above embodiment.

[0153] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the high-precision detection method for key points of textiles based on ultraviolet rays as proposed in the above embodiment is implemented.

[0154] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes 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 can 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 screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0155] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0156] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0157] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0158] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0159] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. 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 high-precision detection method for key points of textiles based on ultraviolet light, characterized in that: The following steps are involved: S1. Determine the wavelength of ultraviolet light for detection based on fluorescence spectrum analysis; S2, constructing a UV fluorescence spectrum feature library for textiles; S3. Conduct a comprehensive analysis of the detection area and adjust the UV light source; S4, using a high-resolution UV camera to collect images of the textile surface; S5, preprocessing the collected ultraviolet fluorescence image; S6, extracting key point features from the preprocessed image; S7, identifying and classifying defects using the extracted key point features; S8. Based on defect recognition, the defect area is accurately located through image segmentation; S9. Output the test results and generate a test report.

2. The high-precision detection method for key points of textiles based on ultraviolet light according to claim 1 is characterized in that: In the step S1, the surface of the textile is irradiated with an ultraviolet light source to stimulate the fluorescence reaction on the surface of the textile, and a spectrum analysis is performed to evaluate the fluorescence response characteristics of the textile after ultraviolet irradiation, and to determine the ultraviolet wavelength for detection.

3. The high-precision detection method of textile key points based on ultraviolet light according to claim 1 is characterized in that: In the step S2, ultraviolet irradiation experiments are performed on textile samples of different batches, materials and dyeing methods, and their fluorescence spectrum characteristics are recorded to form an ultraviolet fluorescence spectrum characteristic library; The feature library contains all variables that may affect the detection accuracy, including factors such as textile material, dye type, and fabric structure.

4. The high-precision detection method for key points of textiles based on ultraviolet light according to claim 1, characterized in that: In the step S3, the ultraviolet intensity distribution of the detection area is detected using an ultraviolet intensity distribution measuring instrument; By adjusting the position, angle and irradiation distance of the UV light source, uniform UV distribution can be achieved to ensure that each inspection point of the textile can be evenly irradiated by UV rays.

5. The high-precision detection method for key points of textiles based on ultraviolet light according to claim 1, characterized in that: In step S5, the preprocessing includes the following process: First, an image filtering algorithm is applied to remove background noise and eliminate ambient light interference; Then, the image enhancement algorithm is used to enhance the contrast of the image and to enhance the image in areas with weaker fluorescence, so that the tiny defects on the surface of the textile can be clearly presented.

6. The high-precision detection method of textile key points based on ultraviolet light according to claim 1, characterized in that: In step S6, when extracting key point features, a spectrum-texture joint distribution model is constructed to integrate the fluorescence response, texture change and shape characteristics of the textile surface, and coupled using weight factors; In the spectral texture joint distribution model, any pixel point on the textile surface is assumed to be , the eigenvalue is calculated by the following formula: ； in, : Pixels The comprehensive eigenvalue of is used to indicate the possibility of this point as a key point; : The eigenvalue based on texture gradient is used to represent the texture complexity of the pixel; : Spectral intensity characteristic value, calculated by the spectral response of the pixel point in the ultraviolet fluorescence image; : Shape eigenvalue, calculated by the significance of the pixel in the surrounding geometric structure; : Weight factor, indicating the importance of texture, spectrum, and shape features in key point extraction.

7. The high-precision detection method of textile key points based on ultraviolet light according to claim 6, characterized in that: In the spectral texture joint distribution model, the calculation of the eigenvalue includes: Texture gradient feature calculation: Spectral intensity feature calculation; Shape feature calculation; Weight optimization and feature fusion.

8. The high-precision detection method for key points of textiles based on ultraviolet light according to claim 1, characterized in that: In step S7, the extracted key point features are input into the machine learning model. Through the training set sample data, the machine learning model learns the characteristics of different types of textile defects and automatically identifies and classifies new samples.

9. The high-precision detection method for key points of textiles based on ultraviolet light according to claim 1, characterized in that: In the step S8, a saliency segmentation model is constructed, and a segmentation mask is dynamically generated by performing multi-dimensional saliency calculation on the spectral response, texture complexity and edge strength of the ultraviolet fluorescence image to achieve accurate positioning of the defect area; Assume the input ultraviolet fluorescence image is , generating a segmentation mask , defined as: ； in, : The saliency function is defined as the comprehensive saliency value of spectrum, texture and edge, which is used to evaluate whether the pixel belongs to the defect area; : Adaptive threshold, dynamically calculated based on the global statistical properties of the saliency function; : Output segmentation mask, where 1 represents the defect area and 0 represents the background area.

10. The high-precision detection method of textile key points based on ultraviolet light according to claim 9, characterized in that: The significance function As shown below: ； in, : Spectral significance, reflecting the abnormality of the textile surface in ultraviolet fluorescence response; : Texture saliency, describing the difference between texture complexity and surroundings; : Edge saliency, used to capture local edge structure characteristics; : Weight factor used to balance the importance of spectral, texture and edge saliency.

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