AI RESEARCH
Boxes2Pixels: Learning Defect Segmentation from Noisy SAM Masks
arXiv CS.CV
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ArXi:2604.11162v1 Announce Type: new Accurate defect segmentation is critical for industrial inspection, yet dense pixel-level annotations are rarely available. A common workaround is to convert inexpensive bounding boxes into pseudo-masks using foundation segmentation models such as the Segment Anything Model (SAM). However, these pseudo-labels are systematically noisy on industrial surfaces, often hallucinating background structure while missing sparse defects.