DEFECT ENHANCEMENT DETECTION METHOD IN COMPLEX BACKGROUNDS BASED ON MIXED ATTENTION
DOI:
https://doi.org/10.35631/JISTM.1142002Keywords:
Attention Mechanism, Feature Enhancement, Defect Detection, Mixed Attention, Lightweight NetworkAbstract
This paper proposes a defect enhancement detection method based on Mixed attention, aiming to address the difficulty of feature extraction caused by complex background interference in metal surface defect detection. The core of this method is the design of a lightweight Mixed Attention Module (MAM). This module integrates channel attention and spatial attention mechanisms in parallel, working collaboratively at multiple levels: channel attention adaptively recalibrates channel feature responses by modeling the interdependencies between feature channels, emphasizing feature maps related to defects; spatial attention focuses on key spatial locations in the feature maps, generating spatial weight masks to highlight defect regions and suppress texture and noise interference from irrelevant backgrounds. Simultaneously, the module employs an efficient structural design, achieving effective capture and fusion of multi-scale contextual information without introducing significant computational overhead, thereby enhancing the discriminative representation of defect features in complex backgrounds. Experimental results demonstrate that this method achieves significant improvements in detection accuracy (mAP) on the NEU-DET and GDUT-DET public metal surface defect datasets.
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