Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive Distillation
Industrial image anomaly detection plays a critical role in intelligent manufacturing by automatically identifying defective products through visual inspection. While unsupervised approaches eliminate dependency on annotated anomaly samples, current teacher–student framework-based methods still face...
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MDPI AG
2025-06-01
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| author | Junxian Li Mingxing Li Shucheng Huang Gang Wang Xinjing Zhao |
| author_facet | Junxian Li Mingxing Li Shucheng Huang Gang Wang Xinjing Zhao |
| author_sort | Junxian Li |
| collection | DOAJ |
| description | Industrial image anomaly detection plays a critical role in intelligent manufacturing by automatically identifying defective products through visual inspection. While unsupervised approaches eliminate dependency on annotated anomaly samples, current teacher–student framework-based methods still face two fundamental limitations: insufficient discriminative capability for structural anomalies and suboptimal anomaly feature decoupling efficiency. To address these challenges, we propose a Synthetic-Anomaly Contrastive Distillation (<i>SACD</i>) framework for industrial anomaly detection. <i>SACD</i> comprises two pivotal components: (1) a reverse distillation (RD) paradigm whereby a pre-trained teacher network extracts hierarchically structured representations, subsequently guiding the student network with inverse architectural configuration to establish hierarchical feature alignment; (2) a group of feature calibration (<i>FeaCali</i>) modules designed to refine the student’s outputs by eliminating anomalous feature responses. During training, <i>SACD</i> adopts a dual-branch strategy, where one branch encodes multi-scale features from defect-free images, while a Siamese anomaly branch processes synthetically corrupted counterparts. <i>FeaCali</i> modules are trained to strip out a student’s anomalous patterns in anomaly branches, enhancing the student network’s exclusive modeling of normal patterns. We construct a dual-objective optimization integrating cross-model distillation loss and intra-model contrastive loss to train <i>SACD</i> for feature alignment and discrepancy amplification. At the inference stage, pixel-wise anomaly scores are computed through multi-layer feature discrepancies between the teacher’s representations and the student’s refined outputs. Comprehensive evaluations on the MVTec AD and BTAD benchmark demonstrate that our method is effective and superior to current knowledge distillation-based approaches. |
| format | Article |
| id | doaj-art-a12a164f06cc4cbd8700a043b50f02c9 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-a12a164f06cc4cbd8700a043b50f02c92025-08-20T03:27:36ZengMDPI AGSensors1424-82202025-06-012512372110.3390/s25123721Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive DistillationJunxian Li0Mingxing Li1Shucheng Huang2Gang Wang3Xinjing Zhao4School of Information Engineering, Yangzhou Polytechnic College, Yangzhou 225009, ChinaSchool of Electrical and Information Engineering, Jiangsu University JingJiang College, Zhenjiang 212013, ChinaSchool of Computer, Jiangsu University of Science and Technology, Zhenjiang 212003, ChinaSchool of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, ChinaSuzhou Zhanchi Tongyang Talent Technology Company, Suzhou 215558, ChinaIndustrial image anomaly detection plays a critical role in intelligent manufacturing by automatically identifying defective products through visual inspection. While unsupervised approaches eliminate dependency on annotated anomaly samples, current teacher–student framework-based methods still face two fundamental limitations: insufficient discriminative capability for structural anomalies and suboptimal anomaly feature decoupling efficiency. To address these challenges, we propose a Synthetic-Anomaly Contrastive Distillation (<i>SACD</i>) framework for industrial anomaly detection. <i>SACD</i> comprises two pivotal components: (1) a reverse distillation (RD) paradigm whereby a pre-trained teacher network extracts hierarchically structured representations, subsequently guiding the student network with inverse architectural configuration to establish hierarchical feature alignment; (2) a group of feature calibration (<i>FeaCali</i>) modules designed to refine the student’s outputs by eliminating anomalous feature responses. During training, <i>SACD</i> adopts a dual-branch strategy, where one branch encodes multi-scale features from defect-free images, while a Siamese anomaly branch processes synthetically corrupted counterparts. <i>FeaCali</i> modules are trained to strip out a student’s anomalous patterns in anomaly branches, enhancing the student network’s exclusive modeling of normal patterns. We construct a dual-objective optimization integrating cross-model distillation loss and intra-model contrastive loss to train <i>SACD</i> for feature alignment and discrepancy amplification. At the inference stage, pixel-wise anomaly scores are computed through multi-layer feature discrepancies between the teacher’s representations and the student’s refined outputs. Comprehensive evaluations on the MVTec AD and BTAD benchmark demonstrate that our method is effective and superior to current knowledge distillation-based approaches.https://www.mdpi.com/1424-8220/25/12/3721anomaly detectionanomaly localizationknowledge distillationfeature refinementabnormal synthesis |
| spellingShingle | Junxian Li Mingxing Li Shucheng Huang Gang Wang Xinjing Zhao Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive Distillation Sensors anomaly detection anomaly localization knowledge distillation feature refinement abnormal synthesis |
| title | Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive Distillation |
| title_full | Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive Distillation |
| title_fullStr | Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive Distillation |
| title_full_unstemmed | Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive Distillation |
| title_short | Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive Distillation |
| title_sort | industrial image anomaly detection via synthetic anomaly contrastive distillation |
| topic | anomaly detection anomaly localization knowledge distillation feature refinement abnormal synthesis |
| url | https://www.mdpi.com/1424-8220/25/12/3721 |
| work_keys_str_mv | AT junxianli industrialimageanomalydetectionviasyntheticanomalycontrastivedistillation AT mingxingli industrialimageanomalydetectionviasyntheticanomalycontrastivedistillation AT shuchenghuang industrialimageanomalydetectionviasyntheticanomalycontrastivedistillation AT gangwang industrialimageanomalydetectionviasyntheticanomalycontrastivedistillation AT xinjingzhao industrialimageanomalydetectionviasyntheticanomalycontrastivedistillation |