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.
@article{arxiv.2503.01234,
title = {Self-Adaptive Gamma Context-Aware SSM-based Model for Metal Defect Detection},
author = {Sijin Sun and Ming Deng and Xingrui Yu and Xingyu Xi and Liangbin Zhao},
journal= {arXiv preprint arXiv:2503.01234},
year = {2025}
}
Comments
8 pages, 5 figures; Accepted for publication at the 2025 International Joint Conference on Neural Networks (IJCNN 2025), Rome, Italy, 30 June - 5 July