## 2 Related Works  
In this section, we review several representative existing texture surface defect detection methods. These can be divided into two groups: conventional texture analysis-based approaches and deep learning-based approaches. \"Texture analysis\" is an umbrella term for a variety of methods based on handcrafted feature extraction and synthesis. It can be further divided into four categories: (i) statistical methods, (ii) structural methods, (iii) spectrum methods, and (iv) model-based methods. Among these, statistical methods and spectrum methods have, to date, proven to be the most practical. Statistical methods focus on representation forms of spatial distribution of gray values, such as histogram statistics [3; 4], autocorrelation functions [5; 6], cooccurrence matrices [7], local binary patterns (LBP) [8], and others. [PERSON] et al. [9] combined spatial gray level cooccurrence matrices with fast discrete curvelet transform to inspect defects on ferrite magnetic tiles, achieving a detection rate of 93.3% with 3.6% false positives. Structural methods commonly regard the texture model as a composition of texture primitives which are arranged according to certain spatial placement rules; a general pipeline of structural methods is the extraction of texture primitives and inference of arrangement rules in sequence. [PERSON] and [PERSON] [10] proposed a multiscale structural similarity index measurement (MS-SSIM) -based method, achieving a 99.1% success rate. Spectrum methods utilize information from different domains, or synthesize spatial domains and transformed domains, to complete the detection task. Fourier transform (FT) [5; 11], wavelet transform (WT) [12; 13] and Gabor transform [14; 15] are most frequently used approaches in practice. [PERSON] and [PERSON] [16; 17] proposed a method combining discrete wavelet transform and edge fusion to segment defects from fabric texture images; they achieved an 89% success rate over 3700 images containing 26 different kinds of defects. Model-based methods mostly concentrate on the understanding of a random field, which is actually a stochastic modeling under a simple function of an array of random variables. The most famous approaches include the autoregressive model [18; 19; 20], Markov random fields [21; 22], fractal models, and others. [PERSON] and [PERSON] [23] used a hidden Markov model to implement a real-time defect detection system for sewer tunnels. Although the aforementioned texture analysis-based methods have seen much success in past decades, they mainly depend on handcrafted features; this imposes a severe restriction on application range. It is additionally worth mentioning that these methods show extreme sensitivity to illumination consistency and noise influence. With the rapid development of deep learning technology, computer vision has made breakthroughs in such application fields as object detection, semantic segmentation, target tracking, etc. Recently, research on deep learning-based surface defect detection has attracted a lot of attention, and a variety of relevant studies have been published [24; 25; 26; 27]. Compared with traditional texture analysis methods, deep learning-based methods possess powerful feature representation learning ability and are skilled at solving complex prob lems. Generally speaking, defect detection methods based on deep learning technology can be categorized into three classes, according to their learning strategies: (i) supervised learning-based methods, (ii) transfer learning-based methods, and (iii) unsupervised learning-based methods. Supervised learning-based methods are suitable for situations where a number of defect-free and defective samples with reasonable annotations are provided. In [28], the authors proposed a twofold joint detection convolutional neural network (CNN) to automatically extract powerful image features for defect detection on the DAGM (German Association for Pattern Recognition) dataset.