提升布料缺陷检测精度与速度，专用于20类缺陷识别。
Fab-ME: A Vision State-Space and Attention-Enhanced Framework for Fabric Defect Detection
- 引入视觉状态空间模块，增强细节与全局信息捕捉。
- 在天池数据集上[email protected]提升3.5%，小目标检测更敏感。
- 适合工业质检场景，兼顾精度与实时性需求。
高效缺陷检测对保障纺织品质量、功能和经济价值至关重要。现有方法在高精度、实时性和全局信息提取方面仍存挑战。为此，本文提出Fab-ME框架，基于YOLOv8s设计，专用于20类布料缺陷的精准检测。创新点包括：在特征融合网络颈部引入跨阶段部分瓶颈视觉状态空间（C2F-VMamba）模块，结合视觉状态空间（VSS）块，提升对复杂细节和全局上下文的捕捉能力，同时保持高处理速度；在特征提取网络末层加入增强型多尺度通道注意力（EMCA）模块，显著增强对小目标的敏感性。在天池布料缺陷检测数据集上的实验表明，Fab-ME相较原始YOLOv8s在[email protected]上提升3.5%，验证了其在精确与高效布料缺陷检测中的有效性。
原文摘要 · Abstract (English)
Effective defect detection is critical for ensuring the quality, functionality, and economic value of textile products. However, existing methods face challenges in achieving high accuracy, real-time performance, and efficient global information extraction. To address these issues, we propose Fab-ME, an advanced framework based on YOLOv8s, specifically designed for the accurate detection of 20 fabric defect types. Our contributions include the introduction of the cross-stage partial bottleneck with two convolutions (C2F) vision state-space (C2F-VMamba) module, which integrates visual state-space (VSS) blocks into the YOLOv8s feature fusion network neck, enhancing the capture of intricate details and global context while maintaining high processing speeds. Additionally, we incorporate an enhanced multi-scale channel attention (EMCA) module into the final layer of the feature extraction network, significantly improving sensitivity to small targets. Experimental results on the Tianchi fabric defect detection dataset demonstrate that Fab-ME achieves a 3.5% improvement in [email protected] compared to the original YOLOv8s, validating its effectiveness for precise and efficient fabric defect detection.
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