## 2 Related Work  
Traditional Surface Defect Detection Methods. The traditional methods for surface defect detection can be categorized into three classes according to the image processing techniques. (a) Statistics-based methods; (b) spectrum-based methods; (c) model-based methods. Statistics-based methods commonly use the grayscale distributions of image regions to describe texture characteristics, such as the gray-level co-occurrence matrix method [4], the autocorrelation method [5], the morphology method [6], and the histogram feature statistics [7]. Spectrum-based methods focus on finding the textural structure of the texture image and are particularly suitable for textures with an obvious structure, such as the Fourier feature method [8], Gabor feature method [9], and wavelet feature method [10]. Model-based methods describe texture patterns by modeling special distributions or other attributes with certain models, such as the fractal body model [11], random field model [12], and back scattering model [13], and so on.  
CNN-based Surface Defect Detection Methods. Many CNN-based methods have emerged in recent years. [PERSON] et al. [14] applied CNN for the first time to detection of steel surface defects and proved that the method of CNN was superior to the traditional SVM classifier. [PERSON] et al. [15] gathered the image of the rail surface and experimented with classical CNN, and the result showed that classical CNN already distinctly outperforms the model-based methods. [PERSON] et al. [16] used deeper convolutional neural networks (DCNNs) to detect railway surface defects. [PERSON] et al. [17] proposed a supervised CNN architecture for image patch classification. [PERSON] et al. [18] and [PERSON] et al. [19] also proposed novel DCNNs architectures that obviously improved the performance of automated defect detection. [PERSON] et al. [20] cut defect images into patches for detection. [PERSON] et al. [21] proposed deep metric learning using triplet networks for defect detection.  
Multi-scale Cascade Network. As CNN becomes deeper, a new research problem emerges: after the information of input images passes through many layers, it can vanish when it reaches the end of the network. Highway Networks [22] bypasses the signal from one layer to the next layer via identity connections. FractalNet [23] repeatedly combines several parallel layer sequences with a different number of convolutional blocks to obtain a large nominal depth while maintaining many short paths in the network. Although these different approaches vary in network topology, they all have common characteristics: they create short paths from the early layers to later layers. In [24; 25; 26; 27], the use of multi-scale features in CNNs through skip connections has been found to be effective for various vision tasks. However, in the task of using CNN for defect detection [15; 16; 17; 18; 19], the network structure of CNN is usually streamlined, and does not use shallow network information.  
MobileNet-v2. To extract richer features, current convolutional neural networks tend to increase the width and depth of the network. These methods have resulted in large network parameters and are difficult to apply on hardware devices with limited resources. MobileNet [28] was proposed to solve this problem. MobileNet-v1 [28] replaces standard convolution with depth separable convolution and divides the traditional convolution process into two steps: filtering and merging. This convolution approach greatly reduces the computational cost and model size and is easier to deploy in resource-constrained environments. The design of MobileNet-v2 [29] is based on MobileNet-v1 and borrows ideas from ResNet [30] to add the residual structure of the shortcut.