## 1 Introduction  
Steel strips are an important industrial material in production and life. The defects on steel strips such as patch, scratch, rolled in scale, etc., are observed in the production process due to equipment, technology and production environment. The presence of these defects can seriously affect the quality and value of the steel strips. The defects on the steel strip surface are very complex. As shown in Figure 0(a), the contrast between some types of defects and the background is quite low in the grayscale image of the steel strip, and there are defects of multiple scales and shapes. Furthermore, there is a high degree of similarity between certain types of defects, and there is also a phenomenon of defect overlap, as shown in Figure 0(b),c. Therefore, defect detection of steel strip surface is very challenging. In the early days, surface defects of steel strips were usually detected through human eyes. This method is quite costly and inefficient. Consequently, the automatic defect detection on the steel strip surface is essential to reduce costs and improve production efficiency [1]. The automatic defect detection methods based on computer vision have shown great advantages in the surface defect detection of steel strips [2,3]. Early automatic defect detection methods usually use feature extractors, such as histogram of oriented gradient (HOG) [4], local binary patterns (LBP) [5], etc. to extract image features. Classifiers such as support vector machines (SVM) [6] and random forest [7] are then used to classify and identify defects. However, those methods mainly rely on ingenious hand-crafted feature design. Additionally, they require a lot of professional knowledge and only work on specific scenes. Therefore, the generalization and robustness of those methods is poor. Deep learning-based methods have been widely used in industrial inspection [8,9]. As one of the most successful algorithms of deep learning, the convolutional neural network (CNN) has achieved great progress in the computer vision area. Features with high abstraction and high representational capabilities can be extracted by CNN through learning the feature distribution of datasets with lots of samples. Compared with earlier feature extractors, the high-level features extracted by CNN have richer semantic information, which helps better classify and detect, and enhances the generalization and robustness of the model. However, some feature information, especially for minor defects, will be lost in the feature extraction process due to convolution and pooling operations by CNN. Therefore, maintaining the feature information of minor defects is very important for multi-scale defect detection during extracting high-level semantic feature information [10]. At present, only a single high-level feature is used for defections in many networks, resulting in poor detection for multi-scale and minor defects. The Feature Pyramid Network (FPN) [11] provides an idea to solve this problem. It fuses high-level semantic features and low-level coarse-grained features by adding a top-down path. The use of multi-level features in detection is beneficial for the detection of multi-scale and minor defects. In order to further shorten the information flow path from low-level features to high-level features, a bottom-up path augmentation [12] is proposed. It improves the utilization of rich location information in low-level features by reducing the number of convolutional layers that need to be traversed between low-level and high-level features. In addition, the atrous spatial pyramid pooling (ASPP) [13] also has a similar pyramid structure by arranging multiple convolutional layers with different dilation rates in parallel. It can learn more contextual information and expand the receptive field. Even though there are a lot of excellent networks, it is still hard for the neural network models to learn defective features of a steel strip for the following two issues.