## 2 Related Work  
### Automatic Defect Detection Method  
In the traditional detection methods, manual features are usually designed to extract the texture, shape, edge, and other information of the images, and then classifiers are trained for defect detection. [PERSON] et al. [17] proposed enhanced local binary patterns (LBP) to resist noise, and utilized the nearest neighbor classifier (NNC) and the SVM for defect identification. [PERSON] et al. [18] used four kinds of statistical features to extract the feature information of the steel strip images and used the enhanced SVM to detect defects in the samples and achieved multi-classification. [PERSON] et al. [19] divided the detection process into two stages. The phase-only Fourier transform is used to generate saliency maps in the first stage, and the defects are extracted by using the saliency maps in the next stage. Although these methods have achieved good performance, they limit the application to specific situations and rely mostly on expertise. Therefore, they generally lack generalization and robustness.  
CNN has created a craze in the computer vision area and achieved great success. Through the learning of a large amount of data, the feature information with deeper levels and stronger performance capabilities can be extracted by CNN. The good portability and generalization of CNN shows great application prospects in industrial detection. [PERSON] et al. [20] proposed a multi-level feature fusion method to detect surface defects of steel strips. The method captures multi-scale information by fusing multi-level features, which improves the detection effect of multi-scale defects. However, they did not consider the importance of different levels of features when performing feature fusion. [PERSON] et al. [21] published a surface defect detection method for steel strips based on attention mechanism and feature fusion. This method uses an attention mechanism to reduce the loss of feature information and applies an ASHF module to fuse high-level features and low-level features.  
However, this method has certain limitations when detecting dense defects. [PERSON] et al. [22] proposed a few-shot segmentation method for metal surface defects segmentation. In this method, the defect segmentation problem is transformed into semantic segmentation of defect parts and background parts by using a triplet encoder. Multi-graph reasoning is then used to infer the similarity between different images for few-shot surface defect segmentation. [PERSON] et al. [23] developed a deep learning-based detection method for surface defects of steel strips. Feature collection and compression modules are applied to enrich multi-scale information firstly in the method, and then a novel Gaussian weighted pooling method is employed to extract ROI features for classification and localization. [PERSON] et al. [24] used a saliency detection method for steel strip defects by combining a decoder and an encoder with an attention mechanism. This method can eliminate background noise and speed up the convergence of the model. [PERSON] et al. [25] improved Faster R-CNN by using deformable convolution, balanced pyramid, and cascaded detection head to improve the defect detection of steel strips. However, most current CNN-based steel strip defect detection methods perform unsatisfactorily in multi-scale defect detection. Therefore, a novel method for multi-scale feature capture and fusion is proposed to address the above-mentioned problem in this paper.