## 1 Introduction  
In recent years, the limitations of manual detection, including low accuracy, poor real-time performance, and high labor intensity, have been addressed through the development of automatic detection methods based on machine vision. This technology has emerged as a fast and reliable alternative to manual detection for numerous surface defects, offering advantages such as high automation, dependability, and reduced subjectivity. Machine vision-based inspection has proven to be highly adaptable to different environmental conditions and can operate continuously with high levels of accuracy and efficiency. Byimplementing machine vision defect detection technology, businesses can benefit from reduced production costs, improved productivity, enhanced product quality, and pave the way for the intelligent transformation of the industrial sector [1]. One of the most significant research projects in the field of industrial vision has long been industrial defect detection. Machine vision algorithms offer numerous techniques for identifying defects, which can be broadly categorized into two groups: conventional techniques and deep learning techniques. It has been successfully used to identify mutant defects on textile surfaces according to [PERSON] et al. [2], who deal with texture defects based on two fundamental structural qualities, regularity and local orientation (anisotropy). According to research by [PERSON] et al. [3], the accurate identification and segmentation of texture surface defects can be achieved through a method utilizing the support vector machine classification technique based on Gabor wavelet features. A VMD mode number optimization method based on the maximum envelope kurtosis was proposed by [PERSON] et al. [4] and has strong generalizability and anti-noise performance. A structure visual detection method based on Faster region convolutional neural network (Faster R-CNN) [5] was proposed by [PERSON] et al. [6], which enabled the simultaneous, quasi-real-time detection of concrete cracks, medium and high-level steel corrosion, bolt corrosion, and five different types of steel delamination damage. With a resolution of 500 \(\times\) 375, this method offers a relatively fast detection speed of 0.03 s per image. [PERSON] et al. [7] proposed a method for the automatic detection of insulator defects using aerial photographs. Their study focused on achieving the precise localization of insulator defects in real inspection environments. To detect strip surface defects, [PERSON] et al. [8] developed a fully convolutional YOLO network based on the YOLO (You Only Look Once) principle. The network offers method support for the instantaneous discovery of strip surface defects. A unified framework for identifying defects in industrial goods or flat surfaces is put out by [PERSON] et al. [9] and is based on an end-to-end learning technique. For defect detection, a framework for end-to-end learning is established. A display line defect detection method based on the fusion of color features was proposed by [PERSON] et al. [10]. This technique has a detection accuracy of over 90% for multi-background offline defects, which is superior to other widely used algorithms in this detection. [PERSON] et al. [11] introduced a hierarchical training-CNN approach with feature alignment, which demonstrated an improved performance. Furthermore, this method has been successfully applied in a real-world case, resulting in significant enhancements. [PERSON] et al. [12] presented a novel automatic defect detection solution called YOLO-attention, based on YOLOv4, utilizing deep learning techniques. This approach achieves both fast and accurate defect detection for the process of Wire Arc Additive Manufacturing (WAAM). [PERSON] et al. [13] proposed a real-time non-destructive evaluation technique that utilizes highly nonlinear solitary waves (HNSWs) and deep learning to detect defects in laminated composites.