Kavli Affiliate: Feng Wang
| First 5 Authors: Zhengyang Lu, Bingjie Lu, Weifan Wang, Feng Wang,
| Summary:
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.
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