Anomaly detection in uncontrolled industrial environments with data preprocessingBoljević, Luka (Avtor) Skočaj, Danijel (Mentor) Computer visionanomaly detectiondata preprocessingindustrial environmentuncontrolled environmentAnomaly detection in industrial applications is commonly evaluated on standard benchmark datasets such as MVTec AD and VisA, whose data were captured under controlled conditions. Performance on these benchmarks does not necessarily transfer to real-world environments. In uncontrolled industrial settings, images often contain multiple objects, varying backgrounds, and inconsistent orientations, which significantly complicates anomaly detection. This thesis addresses these challenges by proposing a three-stage anomaly detection pipeline designed to reduce environmental effects prior to anomaly detection. The pipeline consists of object detection, canonicalization, and anomaly detection, transforming each object into a canonical representation. The approach is evaluated on an electrical insulator defect detection dataset, as well as select categories from the MVTec AD and VisA benchmarks, using unsupervised anomaly detection methods. Experimental results demonstrate that appropriate preprocessing consistently improves anomaly detection performance on the insulator dataset, with measurable improvements on selected MVTec AD and VisA categories.20262026-05-08 14:40:02Magistrsko delo/naloga182384VisID: 38436sl