## 2 Related Work  
### Categories of Surface Defect Detection Algorithms  
According to Author [6], surface defect detection algorithms can be divided into four categories: traditional statistical-based algorithms, spectrum-based algorithms, model-based algorithms, and emerging deep learning algorithms. For deep learning algorithms, three are traditional defect detection algorithms. Only two-dimensional defect detection algorithms are discussed in this paper. For traditional defect detection algorithms, for example edge detection [7], gray level statistics [8], local binary pattern [9], wavelet transform [10], genetic algorithms [11], and the fractal dimension model [12], features are extracted by manually designed feature extractors, then the features are learned by various feature classifiers, such as K nearest neighbors [13], support vector machine [14; 15], Random Forest [16], etc. However, the robustness and real-time performance of these traditional algorithms are poor. The processes of data pre-processing, feature extraction, feature reduction, and classifier selection in traditional algorithms need the experience of experts. When images contain noise or textured backgrounds, the real defect edges may be missed due to noise interference, which does not meet the requirements of on-line detection of surface defects. With the rapid development of artificial intelligence, a computational vision of surface defect recognition and classification has emerged. Convolutional neural networks (CNNs), as a typical deep neural network (DNN), have been widely used in the fields of fault diagnosis, defect detection, and image recognition. Author [17] proposed a Max-Pooling convolutional neural network. Compared to the performance of SVM classifiers, the Max-Pooling convolutional neural network performs at least two times better. Author [18] proposed a semi-supervised learning method using a convolutional neural network (CNN). The proposed method requires fewer labeled samples, and the unlabeled data can be used for training. Author [19] proposed an improved You Only Look Once (YOLO) network. The network achieved a 99% detection rate with speed of 83 FPS on 4655 digital photos of steel strip surfaces. Due to the limitation of data size, it is difficult for large baseline CNNs to be fully trained. Thanks to transfer learning and ImageNet's pre-training networks, it is feasible  
Figure 1: Overview of the strip steel defect detection method. to train few samples datasets on large baseline CNNs by fine-tuning networks' structures on the pre-training networks. Transfer learning is a way to learn from previous tasks and apply the learning to new tasks. Its purpose is to extract knowledge and experience from one or more source tasks, and then apply this to a new target domain. Since 1995, transfer learning has attracted the attention of many researchers. The basic unit of an image is a pixel point; different gradations of pixel point arrangements and combinations together can represent various types of low-level features, such as lines, dots, and colors. By matching and combining these low-level features with each other, high-level image information can be formed, such as texture and geometries. Although high-level information of different images is different, underlying features are similar [20]. This is the basis of image data transfer learning. Author [21] showed that transfer learning can be successfully applied using image data from an entirely different domain. Author [22] proposed a baseline ResNet convolution neural network (CNN) with a multilevel feature fusion network (MFN) module. On the NEU database, ResNet50 with the MFN module achieves a 99.7% detection rate with a speed of 6.1 FPS. Author [23] proposed a crack detection method based on a deep fully convolutional network (FCN) with VGG16 backbones. The network achieved about 90% average precision on a subset of 500 annotated 227 \(\times\) 227-pixel crack-labeled images.