LFRNet: Localizing, Focus, and Refinement Network for Salient Object Detection of Surface Defects
Bin Wan, Xiaofei Zhou, Bolun Zheng, Haibing Yin, Zunjie Zhu, Hongkui Wang, Yaoqi Sun, Jiyong Zhang, Chenggang Yan
Abstract
Salient object detection of surface defects is one of the surface defect detection tasks, which aims at highlighting the defect regions from the surface of strip steel, magnetic tale, road, and so on. However, the performance of existing methods degrades dramatically when dealing with complex scenarios, such as low contrast of defect regions and various defect shapes. Therefore, in this article, we propose a novel saliency model, namely, localizing, focus, and refinement network (LFRNet), which consists of the semantic-guided localizing module, the context-driven focus module, and the edge-aware refinement (ER) module. First, the semantic-guided localizing module deploys the graph reasoning (GR) unit and the global attention (GA) unit to localize the potential defect regions from a global view. Second, the context-driven focus module employs the split context (SC) unit and the mutual attention (MA) unit to perform the identification process via the introduction of spatial detail features. Finally, to further improve the accuracy of the detection results, we deploy the ER module, which introduces the boundary cues via the edge generation (EG) unit and aggregates the localizing result, the focus results, and the edge information into the high-quality detection map. Extensive experiments on four public defect datasets clearly show the effectiveness and superiority of the proposed LFRNet, where the LFRNet obtains an improvement of 4.1%, 5.7%, 1.0%, and 0.8% on F-measure (FM), weighted FM (WF), E-measure (EM), and structure-measure (SM), respectively, compared with the top-level method: AEP.