LAUSR

Recently, researchers have paid more attention to salient object detection (SOD) of strip steel surface defects, but there are lots of difficulties, such as a mass of noise, blurry defect boundaries, complex backgrounds, and various types of defects. The existing image SOD methods fail to overcome the above challenging scenes. To address them, in this article, we propose a novel two-stage edge reuse network (TSERNet), which consists of two stages, i.e., prediction and refinement. In the first prediction stage, we construct a primary net based on the encoder–decoder architecture. The encoder not only extracts multiscale features but also generates an edge map. In the decoder, we propose a novel edge-aware foreground–background integration (EFBI) module to distinguish the foreground and the background through edge features and reverse the attention mechanism, and then exploit the decoder to generate an initial saliency map. In the second refinement stage, we construct a sub-net based on the same architecture as that of the first stage. Its encoder extracts features from the initial saliency map, and its decoder deploys the edge-aware refinement module (ERM), which reuses the edge map generated from the first stage, to enhance these features for purifying the initial saliency map, resulting in the final saliency map. Comprehensive experiments on the public dataset show that our proposed TSERNet is consistently superior to 22 relevant state-of-the-art methods. The code and results of our method are available at https://github.com/monxxcn/TSERNet.