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arXiv · 2610.04392

Detecting Defects that Matter: An Application-Driven Benchmark for Anomaly Detection in Manufacturing and Retail Logistics (VAND 4.0 Challenge)

Lars Heckler-Kram·Dorian Henning·Ashwin Vaidya·Jan-Hendrik Neudeck·Ulla Scheler·Anton Milan·Samet Akcay·Paula Ramos·Sebastian Höfer
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Abstract

Existing Anomaly Detection benchmarks are saturated and often unrealistic. As part of the VAND 4.0 Challenge, we introduce a hidden-test, application-driven benchmark across two deployment-critical domains: industrial manufacturing and retail logistics. In the Industrial Track (MVTec AD 2), the results reveal that unsupervised anomaly segmentation remains challenging: the best regular-setting method achieves only ~57\% pixel-level $SegF_1$, indicating substantial room for improvement. Zero-shot approaches trail by ~15 $SegF_1$ points, confirming that task-specific training on normal data remains essential for precise defect localization. Robustness to distribution shifts remains a key open challenge and DINOv3-backbones clearly dominate this track. In the Retail Track (Kaputt 2), the results reveal that (1) supervised defect detection is approaching saturation for common defect types; (2) the best off-the-shelf VLM approach trails specialized models by ~28 AP, confirming that currently VLMs cannot replace fine-tuned detectors, (3) reference images did not prove helpful for top-performing approaches. Performance collapses on rare defects (spillage ~53 AP, missing units ~27 AP), where the supervised ceiling is bounded by data availability. To drive future progress in this domain, we provide a new low-prevalence retail AD dataset (Kaputt-Rare). Across both tracks, computational efficiency is assessed as a first-class metric combining performance, throughput, memory, and power consumption. We introduce a novel metric for measuring efficiency and reveal that that top-performing methods rely on heavy architectures while efficiency is largely neglected. Overall, we conclude that the community needs (a) more efficiency-aware method development, and (b) true anomaly detection approaches for rare defects and shifting conditions. https://sites.google.com/view/vand4-cvpr2026/challenge

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Lars Heckler-Kram, Dorian Henning, Ashwin Vaidya, Jan-Hendrik Neudeck, Ulla Scheler, Anton Milan, Samet Akcay, Paula Ramos, Sebastian Höfer. 2026-10-03. Detecting Defects that Matter: An Application-Driven Benchmark for Anomaly Detection in Manufacturing and Retail Logistics (VAND 4.0 Challenge). https://arxiv.org/abs/2610.04392

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