自适应伽马校正+状态空间模型，提升金属缺陷检测精度
Self-Adaptive Gamma Context-Aware SSM-based Model for Metal Defect Detection
- 引入动态伽马校正模块，增强灰度特征表达
- 在3个数据集上[email protected]提升最高达27.6%
- 适合工业场景中复杂光照与缺陷形态的检测任务
金属缺陷检测在工业质量控制中至关重要，但现有方法难以应对灰度变化和复杂缺陷状态，影响鲁棒性。本文提出自适应伽马上下文感知的状态空间模型（GCM-DET），融合动态伽马校正（GC）模块以增强灰度表征并优化特征提取，实现精确缺陷重建；采用状态空间搜索管理（SSM）架构捕捉鲁棒的多尺度特征，有效处理不同形状与尺度的缺陷；使用焦点损失（Focal Loss）缓解类别不平衡问题，提升检测精度。此外，构建了专为集装箱维护设计的CD5-DET数据集，包含显著灰度变化与复杂缺陷模式。实验结果表明，该模型在CD5-DET、NEU-DET和GC10-DET数据集上[email protected]分别提升27.6%、6.6%和2.6%。
原文摘要 · Abstract (English)
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 [email protected] gains of 27.6\%, 6.6\%, and 2.6\% on the CD5-DET, NEU-DET, and GC10-DET datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。