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SMINet:Semantics-aware multi-level feature interaction network for surface defect detection.
- Source :
-
Engineering Applications of Artificial Intelligence . Aug2023:Part C, Vol. 123, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 123
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 164285234
- Full Text :
- https://doi.org/10.1016/j.engappai.2023.106474