用可微分匹配实现端到端布料缺陷检测，定位更准且适合工业部署。
Differentiable NMS via Sinkhorn Matching for End-to-End Fabric Defect Detection
- 将NMS重构成可微分的二部匹配问题，通过Sinkhorn算法保持梯度连续
- 在天池数据集上显著提升定位精度，同时保持实时推理速度
- 适用于多种模型架构，可迁移至通用目标检测任务
布料缺陷检测面临两大挑战：传统非极大值抑制破坏梯度流，阻碍真正的端到端学习；在工业规模下获取像素级标注成本过高。为此，我们提出一种针对布料缺陷检测的可微分NMS框架，通过端到端优化实现更优的定位精度。将NMS重构为可通过Sinkhorn-Knopp算法求解的可微分二部匹配问题，确保网络中梯度流动不间断。该方法结合提案质量、特征相似性和空间关系，专门应对布料缺陷不规则形态与模糊边界。熵约束的掩码精炼机制通过合理的不确定性建模进一步提升定位精度。在天池布料缺陷数据集上的大量实验表明，该方法显著优于现有方法，同时保持适合工业部署的实时速度。该框架对不同模型架构表现出良好适应性，并能有效泛化至通用目标检测任务。
原文摘要 · Abstract (English)
Fabric defect detection confronts two fundamental challenges. First, conventional non-maximum suppression disrupts gradient flow, which hinders genuine end-to-end learning. Second, acquiring pixel-level annotations at industrial scale is prohibitively costly. Addressing these limitations, we propose a differentiable NMS framework for fabric defect detection that achieves superior localization precision through end-to-end optimization. We reformulate NMS as a differentiable bipartite matching problem solved through the Sinkhorn-Knopp algorithm, maintaining uninterrupted gradient flow throughout the network. This approach specifically targets the irregular morphologies and ambiguous boundaries of fabric defects by integrating proposal quality, feature similarity, and spatial relationships. Our entropy-constrained mask refinement mechanism further enhances localization precision through principled uncertainty modeling. Extensive experiments on the Tianchi fabric defect dataset demonstrate significant performance improvements over existing methods while maintaining real-time speeds suitable for industrial deployment. The framework exhibits remarkable adaptability across different architectures and generalizes effectively to general object detection tasks.
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。