## 2 Related Works  
Some general semantic segmentation networks are applied in surface defect detection, including FCN, SegNet, Unet, and DeepLab. [PERSON] et al. [14] designed a classic semantic segmentation network, that is FCN. [PERSON] et al. [22] proposed a two-stage surface defect segmentation network based on FCN. The first stage uses a lightweight FCN to quickly obtain rough defect areas, and then the output of the first stage is used as the input to the second stage to refine the defect segmentation results. The network achieved an average pixel accuracy of 95.9934% on the public dataset named DAGM 2007. [PERSON] and [PERSON] [23] used a FCN based on VGG16 to segment the surface cracks of concrete and achieved an average pixel accuracy of 90%. The SegNet proposed by [PERSON] et al. [15] applies the pooling position of the pooling layer from the encoding process to the decoding process. In the decoding process, a sparse feature map is generated by upsampling according to the pooling position then, the convolution layer is used to restore the dense feature map; finally, an accurate pixel location is obtained by multiple upsampling and convolution. However, FCN directly uses the deconvolution to obtain the upsampled feature map and then adds it to the feature map in the encoding process, so the structure of SegNet is smaller than that of FCN. [PERSON] et al. [24] proposed FL-SegNet, combining SegNet with focal loss, and applied it in detecting various defects in the lining of highway tunnels. [PERSON] et al. [25] designed a network based on SegNet to detect nanometer-scale crystallographic defects in electron micrographs and obtained results with pixel accuracies across all three types of steel defects, namely, 91.60 \(\pm\) 1.77% on dislocation, 93.39 \(\pm\) 1.00% on precipitates, and 98.85 \(\pm\) 0.56% on voids. [PERSON] et al. [26] built DeepCrack based on the encoder-decoder architecture of SegNet for crack detection. In this network, the multi-scale deep features learned in the hierarchical convolution stage are fused together to detect fine cracks. DeepCrack achieved F-measure values over 87% on three challenging datasets. The Unet proposed by [PERSON] et al. [15] has the classic and standard encoder-decoder structure. Unet is characterized by the introduction of feature channels, which combines the feature maps of the decoding and encoding stages and helps restore segmentation details. This network has achieved good results in semantic segmentation tasks and has been integrated into many industrial inspection systems because it is lightweight. [PERSON] et al. [27] proposed the MCuePush Unet based on Unet to detect the surface defects of magnetic tiles. The input of the network is generated from the three-channel images of the MCue module, namely, a saliency image and two original images. The MCuePush Unet achieved good results on the Magnetic Tiles dataset. [PERSON] et al. [28] proposed a surface defect detection network for concrete based on improved Unet. The Dense Block module is used in the encoder of the network, and the feature channels add features pixel by pixel but do not concatenate features. This network achieved an average pixel accuracy of 91.59% and an average intersection over union (IoU) of 84.53% on a concrete database with 2750 images (with 504 \(\times\) 376 pixels) and four types of defects. In [29], a segmentation network based on Unet and the ResNet [9] module was proposed by [PERSON] for the detection of conductive particles in the TFT-LCD manufacturing process. The DeepLab [17] series of networks are some of the most successful semantic segmentation networks. DeepLab has proposed unique solutions for some problems, such as extracting semantic and multi-scale features. The latest network named DeepLab v3+ uses ResNet-101 to extract features, and the ASPP is used to sample features by hole convolutions with different dilation rates. ASPP can extract the multi-scale features of images. To automatically detect the surface defects of different steels, [PERSON] et al.