Automated quality inspection of cluttered, multi-instance e-grocery stock containers is challenging because packaging clutter, product boundaries, container wear, and loose debris generate local anomaly responses that do not correspond to genuine quality issues. This thesis develops a modular reference-based inspection pipeline in which normality for each stock keeping unit (SKU) is modelled through automatically constructed and sampled reference sets. Query images are scored by patch-level nearest-neighbour matching of DINOv2 features against a reference memory bank, following the AnomalyDINO paradigm. Edge downweighting and crop-level debris filtering suppress boundary- and debris-driven false positives, while an optical character recognition (OCR)-based branch additionally addresses visually subtle wrong-SKU substitutions. Evaluated on 2,996 normal and 1,657 issue images across 20 SKU classes and five issue families, the full pipeline increases precision from 0.511 to 0.834 and reduces false positives from 1,414 to 201 relative to the core detector (F1-score: 0.705). This improvement comes at a recall cost, with recall decreasing from 0.893 to 0.610. The full pipeline achieves a 99th-percentile end-to-end latency of 1.4 s, within the 10 s operational constraint. The results demonstrate that robust inspection in this setting requires treating false-positive mitigation as a core system requirement rather than relying on raw anomaly sensitivity alone. ... Read more