多尺度自监督特征融合的工业视觉缺陷检测方法OA

Aiming at the problem that visual defect detection models require retraining and parameter tuning after product category switching,we propose an industrial visual defect detection method based on multi-scale self-supervised feature fusion.The method employs a frozen self-supervised visual backbone network to extract shallow,middle,and deep patch token features in parallel via multiple hooks,and hierarchically constructs a core subset memory bank for each layer.It then fuses the nearest neighbor distances from all layers into a unified anomaly map using an equal-weight score-level fusion mechanism,thereby avoiding category-wise parameter tuning and retraining.On all 15 categories of the MVTec-AD dataset under 3 random seeds,the image-level AUROC reaches 0.987 4±0.001 7,with a cross-seed standard deviation that is only 11％-20％of that of the same backbone baseline,while the region-level PRO achieves 0.816 3±0.012 1.Ablation study results attribute the reduction in cross-seed standard deviation primarily to the equal-weight score-level fusion mechanism of multiple hooks;the standard deviation reduction of multi-layer compared to single-layer L9 across all 15 categories is approximately 45％.When zero-shot transferred to all 12 categories of the VisA dataset,the P-AUROC reaches 0.992 9;on the first 6 categories of the Real-IAD dataset,the P-AUROC achieves 0.984 7.On a self-collected engine cylinder liner defect image dataset,the P-AUROC reaches 0.967 3±0.001 5.In half-precision mode,the inference time per image is about 25 ms,which can meet the takt time requirements of typical medium-speed production lines.