Sample-Centric Multi-Task Learning for Detection and Segmentation of Industrial Surface Defects

• URL: http://arxiv.org/abs/2510.13226v1
• Date: Wed, 15 Oct 2025 07:24:26 GMT
• Title: Sample-Centric Multi-Task Learning for Detection and Segmentation of Industrial Surface Defects
• Authors: Hang-Cheng Dong, Yibo Jiao, Fupeng Wei, Guodong Liu, Dong Ye, Bingguo Liu,
• Abstract summary: Industrial surface defect inspection for sample-wise quality control (QC) must simultaneously decide whether a given sample contains defects and localize those defects.<n>Existing models achieve strong pixel-overlap metrics but exhibit insufficient stability at the sample level.<n>We propose a sample-centric multi-task learning framework and evaluation suite.
• Score: 3.0497940633809395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Industrial surface defect inspection for sample-wise quality control (QC) must simultaneously decide whether a given sample contains defects and localize those defects spatially. In real production lines, extreme foreground-background imbalance, defect sparsity with a long-tailed scale distribution, and low contrast are common. As a result, pixel-centric training and evaluation are easily dominated by large homogeneous regions, making it difficult to drive models to attend to small or low-contrast defects-one of the main bottlenecks for deployment. Empirically, existing models achieve strong pixel-overlap metrics (e.g., mIoU) but exhibit insufficient stability at the sample level, especially for sparse or slender defects. The root cause is a mismatch between the optimization objective and the granularity of QC decisions. To address this, we propose a sample-centric multi-task learning framework and evaluation suite. Built on a shared-encoder architecture, the method jointly learns sample-level defect classification and pixel-level mask localization. Sample-level supervision modulates the feature distribution and, at the gradient level, continually boosts recall for small and low-contrast defects, while the segmentation branch preserves boundary and shape details to enhance per-sample decision stability and reduce misses. For evaluation, we propose decision-linked metrics, Seg_mIoU and Seg_Recall, which remove the bias of classical mIoU caused by empty or true-negative samples and tightly couple localization quality with sample-level decisions. Experiments on two benchmark datasets demonstrate that our approach substantially improves the reliability of sample-level decisions and the completeness of defect localization.

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