Industrial anomaly detection (IAD), aiming at automatically identifying abnormal patterns that deviate from the normal manufacturing process, plays a critical role in ensuring product quality and equipment safety for intelligent manufacturing systems. In this work, we delve into exploring the generalized and subtle-pattern awarded defect detection. We also propose a visual expert-guided multi-scale anomaly detection method. As the extracted regions often exhibit subtle and vague features that hamper the precise and reliable detection, we leverage the established super-resolution technique to enhance the spatial resolution and recover fine-grained details. It facilitates more discriminative defect representation and improves the model’s capacity at localize anomalies at finer scales. The multi-scale fusion module is constructed by the graph attention network. It aggregates the suspicious regions across different scales by modeling their inter-scale dependencies and contextual relationships. As it dynamically weights and localities those features, it preserves both the micro irregularities and macro structural deviations, hence offering comprehensive anomaly information. Extensive experiments under zero-shot and few-shot settings were conducted on several public datasets. The results demonstrate that the proposed method consistently outperforms existing mainstream approaches in both image-level and pixel-level anomaly detection, achieving pixel-level values of 98.6% and 98.1% under the 4-shot setting on two major benchmarks, and 94.6% under the zero-shot setting, with particularly strong capability in detecting subtle defects on fine-grained textures. It also exhibits enhanced robustness and generalization in cross-domain transfer scenarios.
Zhu et al. (Wed,) studied this question.