Distribution-free false-alarm calibration and chance-corrected spatial evaluation for industrial anomaly detection

[2608.15090] Distribution-free false-alarm calibration and chance-corrected spatial evaluation for industrial anomaly detection Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2608.15090 (cs) [Submitted on 15 Aug 2026] Title: Distribution-free false-alarm calibration and chance-corrected spatial evaluation for industrial anomaly detection Authors: Jie Deng View a PDF of the paper titled Distribution-free false-alarm calibration and chance-corrected spatial evaluation for industrial anomaly detection, by Jie Deng View PDF HTML (experimental) Abstract: Studies of industrial visual inspection commonly report the area under the receiver operating characteristic curve (AUROC) and the overlap between anomaly maps and defect masks. Neither measure specifies the false-alarm rate at a selected threshold, while recurrent defect locations and mask geometry can inflate overlap. We combine a distribution-free upper tolerance threshold with a paired-minus-crossed spatial test. This test compares each detector's score-contributing locations with the matched defect mask and with masks from other images; the difference in rates defines spatial-evidence lift relative to the empirical chance-overlap rate. We evaluate three detectors on 120 point-defect images from three ISP-AD modalities and three fixed data splits. Of 378 alarms, 230 overlap the matched mask. Paired and crossed rates are nevertheless similar in eight of nine detector--modality cells; only DINOv2--ASM has a positive 95\% bootstrap lower bound (lift 0.259, 95\% interval 0.159--0.347). On the independent Magnetic Tile Defect dataset, the same analysis gives lifts of 0.203 (0.169--0.236) for Wide ResNet-50 (WRN50) patch memory and 0.231 (0.202--0.262) for Vision Transformer B/16 (ViT-B/16) patch memory, with one-sided permutation $p=10^{-5}$ for both. When crossed masks are restricted to the same defect class, the lifts remain 0.185 and 0.210. Exact sample planning shows that, with 150 calibration normals, a 95\%-confidence distribution-free claim is supported only for target false-positive rates of 1.98\% or higher; a 1\% target requires at least 299 normals. The results support reporting operating-point performance and chance-corrected spatial evidence alongside AUROC and raw mask overlap. Comments: 15 pages, 3 figures, 9 tables Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Machine Learning (cs.LG) Cite as: arXiv:2608.15090 [cs.CV] (or arXiv:2608.15090v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.15090 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jie Deng [ view email ] [v1] Sat, 15 Aug 2026 07:21:05 UTC (187 KB) Full-text links: Access Paper: View a PDF of the paper titled Distribution-free false-alarm calibration and chance-corrected spatial evaluation for industrial anomaly detection, by Jie Deng View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-08 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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