SMINet:Semantics-aware multi-level feature interaction network for surface defect detection

To boost the product quality, numerous saliency-based surface defect detection methods have been devoted to the areas of industrial production, construction consumable, road construction. However, the existing salient object detection (SOD) methods not only consume a significant amount of computing resources but also fail to meet the detection efficiency requirements of enterprises. Therefore, this paper proposes a lightweight semantics-aware multi-level feature interaction network (SMINet), to address the above issues. In the encoder phase, we integrate multiple adjacent level features in the cross-layer feature fusion (CFF) module to alleviate the discrepancy between multi-scale features. In the decoder phase, we first employ the semantic-aware feature extraction (SFE) module to mine the location cues embedded in the high-level features. Afterwards, we introduce the detail-aware context attention (DCA) module based on the attention mechanism to recover more spatial details. Extensive experiments on four surface defect datasets validate that our SMINet outperforms the existing state-of-the-art methods.

Authors

• Bin Wan School of Control Science and Engineering, Shandong University, China
• Xiaofei Zhou Hangzhou Dianzi University
• Yaoqi Sun Hangzhou Dianzi University Lishui Institute of Hangzhou Dianzi University
• Zunjie Zhu
• Haibing Yin Hangzhou Dianzi University Lishui Institute of Hangzhou Dianzi University
• Ji Hu
• Jiyong Zhang
• Chenggang Yan Hangzhou Dianzi University Lishui Institute of Hangzhou Dianzi University

Keywords

• Surface defect detection
• Salient object detection
• Cross-layer feature fusion
• Semantic-aware feature extraction