Surface Anomaly Detection on Shock Absorber Connecting Rods Using an Enhanced PatchCore Algorithm

Abstract

The automatic surface quality inspection of shock absorber connecting rods is crucial for ensuring vehicle safety and performance. This paper proposes an enhanced PatchCore algorithm for unsupervised anomaly detection, which adopts a multi-level feature processing and fusion strategy of hierarchical processing module (HPM) and adaptive feature fusion module (AFFM) to capture multi-scale anomalies, and uses an adaptive greedy coreset sampling method to improve local density estimation for subtle defect detection. The ablation study shows that our enhanced feature extraction framework improves spatial level performance, while the optimized sampling strategy enhances the accuracy of small anomaly detection. Experiments show that our method has superior performance in anomaly detection for shock absorber connecting rods.