Self-Adaptive Gamma Context-Aware SSM-based Model for Metal Defect Detection
This work addresses robust defect detection for industrial quality assurance in metal inspection, with incremental improvements over existing methods.
The paper tackles metal defect detection by proposing a Self-Adaptive Gamma Context-Aware SSM-based model (GCM-DET) to address grayscale variations and complex defect states, achieving mAP@0.5 gains of 27.6%, 6.6%, and 2.6% on three datasets including the new CD5-DET dataset.
Metal defect detection is critical in industrial quality assurance, yet existing methods struggle with grayscale variations and complex defect states, limiting its robustness. To address these challenges, this paper proposes a Self-Adaptive Gamma Context-Aware SSM-based model(GCM-DET). This advanced detection framework integrating a Dynamic Gamma Correction (GC) module to enhance grayscale representation and optimize feature extraction for precise defect reconstruction. A State-Space Search Management (SSM) architecture captures robust multi-scale features, effectively handling defects of varying shapes and scales. Focal Loss is employed to mitigate class imbalance and refine detection accuracy. Additionally, the CD5-DET dataset is introduced, specifically designed for port container maintenance, featuring significant grayscale variations and intricate defect patterns. Experimental results demonstrate that the proposed model achieves substantial improvements, with mAP@0.5 gains of 27.6\%, 6.6\%, and 2.6\% on the CD5-DET, NEU-DET, and GC10-DET datasets.