## 2 Related Work  
### Methods on Defect Recognition  
Vision-based defect recognition can be loosely divided into designed-feature-based methods and learned-feature-based methods [6]. In particular, the former can be further separated into four types, statistical methods, filter-based methods, structural methods, and model-based methods, according to the defects texture [7]. Typically, most scholars indicated that the operator relied on the perception of the defects and achieved a pleasing performance. For example, [PERSON] et al. [8] extracted the distribution features of leather defects, such as mean, variance, skewness, kurtosis, and lower and higher quartile values. The authors aimed to select the most suitable features and to eliminate the redundant information using the Kolmogorov-Smirnov test and percentile thresholding approach. However, the experimental results indicated that the above-mentioned methods are sensitive to imbalanced training data and the diversity of the defect area. [PERSON] et al. [9] applied a gray-level co-occurrence matrix (GLCM) to extract the defect features of welding and fed them into an artificial neural network (ANN) for classification. [PERSON] et al. [10] proposed an optimal multi-feature-set fusion with an improved random forest algorithm (OMFF-RF), which applied GLCM and a histogram of oriented gradient (HOG) as the feature extractors. However, this method might be easily affected by background noises. [PERSON] et al. [11] utilized gradient-only co-occurrence matrices (GOCM) for extruded aluminum profiles classification. On the other hand, [PERSON] et al. [12] implemented a decision tree as the binary classification for defect and defect-free in the first stage, and introduced a multi-class SVM with an extended LBP-based operator to yield a sufficient performance in which the time consumed was acceptable for steel manufacturers. [PERSON] et al. [13] improved the efficiency of the local binary pattern (LBP) algorithm by providing an improved multi-block local binary pattern algorithm. Besides, [PERSON] et al. [14] applied a completed local ternary pattern (CLTP) as the feature extractor and adopted a binary-tree-based SVM as the classifier for weld defect detection. Succinctly, this method requires manual coordination for complicated weld images. On a similar note, [PERSON] et. al. [1] implemented an adjacent evaluation completed local binary pattern (AECLBP) to eliminate the interference of noise. Nevertheless, the experimental results showed that AECLBP is sensitive to Gaussian noise. To overcome the above-mentioned challenge, [PERSON] et al. [15] proposed multi-type statistical features and enhanced twin support vector machines that are insensitive to affine transformation in scale and rotation. In a nutshell, most of the aforementioned approaches have a great influence on noise interference and affine transformation. Thus far, plenty of works that adopted the concept of convolutional neural networks (CNNs) have been developed since the pioneering work carried out by [PERSON] [16] in 1998. Undoubtedly, learned-feature-based methods can describe the feature automatically with lower prior knowledge and achieve a breakthrough performance in most aspects. For instance, [PERSON] et al. [17] used a Max-Pooling Convolutional Neural Networks for steel defect classification. [PERSON] et al. [18] applied CNN with the Gaussian kernel to eliminate noises from raw data. Besides, [PERSON] et al. [19] proposed a VGG-like model with a class activation map to observe the explainability of the CNN and received an outstanding performance in steel surface defect tasks. In a similar fashion, [PERSON] et al. [20] optimized the pre-trained VGG16 and visualized the intermediate activations of the CNN model. In addition, [PERSON] et al. [21] introduced the classification priority network (CPN) with multi-group convolutional neural network (MG-CNN), which yielded promising results in hot-rolled steels. However, the above-discussed work requires a large number of training samples. [PERSON] et al.