TRRC-HETMM: Risk-Controlled Texture-Disentangled Mutual Matching for Industrial Anomaly Detection

Industrial chip inspection must jointly limit missed defects, over-inspection of normal products, and computational cost. This balance is difficult when normal texture appears primarily on the carrier strip, which also belongs to the valid inspection region and therefore cannot simply be masked out. We propose Texture Reweighting and Risk Control for HETMM (TRRC-HETMM), combining texture-disentangled mutual matching, miss-risk calibration, and safe dynamic computation. The method estimates position-conditioned variation subspaces from normal templates and reweights residuals within and orthogonal to those subspaces. Independently calibrated release thresholds and guard rules govern fast decisions and fallback to full matching. Controlled high-frequency suppression and an equal-energy residual-shuffling control further support the contribution of normal strip texture to over-inspection. On a sealed industrial test set with 1,000 defective and 19,000 normal images, the frozen system misses 4 defective samples and over-inspects 2,765 normal samples, corresponding to rates of 0.40% and 14.55%, respectively. At the same four-miss operating point, HETMM over-inspects 6,718 normal samples; TRRC-HETMM reduces this number by 58.84%. Experiments on public benchmarks further assess generalization across object categories.