Self-Adaptive Gamma Context-Aware SSM-based Model for Metal Defect Detection

• URL: http://arxiv.org/abs/2503.01234v2
• Date: Thu, 06 Mar 2025 07:11:32 GMT
• Title: Self-Adaptive Gamma Context-Aware SSM-based Model for Metal Defect Detection
• Authors: Sijin Sun, Ming Deng, Xingrui Yu, Xinyu Xi, Liangbin Zhao,
• Abstract summary: Metal defect detection is critical in industrial quality assurance.<n>Existing methods struggle with grayscale variations and complex defect states.<n>This paper proposes a Self-Adaptive Gamma Context-Aware SSM-based model.
• Score: 3.5792989228178897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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