Rethinking Automated Visual Quality Control: A Systematic Benchmark of State-of-the-Art Anomaly Detection Methods for Assembled PCB Inspection

Abstract

This work benchmarks four state-of-the-art unsupervised anomaly detection methods CFA, DFM, FRE, and PatchCore for automated PCB inspection under a strict one-class learning protocol. All models were trained exclusively on nominal samples and evaluated using default configurations to reflect realistic industrial deployment. Experiments on the MVTec AD Transistor dataset and a high-density Raspberry Pi PCB dataset assessed imageand pixel-level performance using AUROC, F1-Score, and AUPRO. PatchCore achieved the strongest image-level discrimination, CFA provided the most balanced detection–localization trade-off, FRE showed sensitivity to localized structural defects, and DFM performed well globally but with weaker boundary precision. Results demonstrate the practical viability of unsupervised deep anomaly detection for modern AOI systems in dynamic manufacturing environments.