RGB Ensemble Strategies for Unsupervised Industrial Anomaly Detection on the AutoVI Dataset

Automotive inspection in real production lines requires robust detection of rare and diverse 2 defects. Fully supervised methods are often unfeasible because defective samples are scarce 3 and heterogeneous. This work benchmarks recent unsupervised anomaly detection (UAD) 4 methods on AutoVI, a real industrial dataset covering six automotive inspection tasks 5 with challenging lighting, cluttered backgrounds, and multiple viewpoints. We establish 6 RGB and pseudo-depth baselines for seven UAD models under a unified training and 7 evaluation protocol, training exclusively on defect-free samples with z-score calibration for 8 fair comparison. On top of these baselines we study late-fusion ensembles that combine 9 complementary detectors within RGB and across modalities, at both image-score and pixel- 10 map level, reporting AUROC, AP, TPR@TNR, and pixel-level sPRO/AUsPRO at 5% false 11 positive rate. The main finding is that RGB-only late-fusion ensembles consistently improve 12 pixel-level localization, often recovering defect coverage where all individual models fail. 13 Combining RGB with monocular pseudo-depth through the same scheme, by contrast, 14 does not yield systematic gains and is highly sensitive to the quality of the estimated depth 15 channel. These results, validated with statistical significance testing across three random 16 seeds, provide practical guidance for composing UAD pipelines in automotive inspection.