An Attention-Based Network for Textured Surface Anomaly Detection
Source abstract
Abstract
Textured surface anomaly detection is a significant task in industrial scenarios. In order to further improve the detection performance, we proposed a novel two-stage approach with an attention mechanism. Firstly, in the segmentation network, the feature extraction and anomaly attention modules are designed to capture the detail information as much as possible and focus on the anomalies, respectively. To strike dynamic balances between these two parts, an adaptive scheme where learnable parameters are gradually optimized is introduced. Subsequently, the weights of the segmentation network are frozen, and the outputs are fed into the classification network, which is trained independently in this stage. Finally, we evaluate the proposed approach on DAGM 2007 dataset which consists of diverse textured surfaces with weakly-labeled anomalies, and the experiments demonstrate that our method can achieve 100% detection rates in terms of TPR (True Positive Rate) and TNR (True Negative Rate).
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Textile materials and evaluations · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automation, Northwestern Polytechnical University, Xi’an 710129, China
References (28)
Show source-provided references
1. Masci, J., Meier, U., Ciresan, D., Schmidhuber, J., and Fricout, G. (2012, January 10–15). Steel Defect Classification with Max-Pooling Convolutional Neural Networks. Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia.10.1109/IJCNN.2012.6252468 ↗
2. Automatic defect detection on hot-rolled flat steel products10.1109/TIM.2012.2218677 ↗
3. Napoletano, P., Piccoli, F., and Schettini, R. (2018). Anomaly detection in nanofibrous materials by cnn-based self-similarity. Sensors, 18.10.3390/s18010209 ↗
4. Liu, G.K., Yang, N., Guo, L., Guo, S.P., and Chen, Z. (2020). A One-Stage Approach for Surface Anomaly Detection with Background Suppression Strategies. Sensors, 20.10.3390/s20071829 ↗
5. Optical technique for the automatic detection and measurement of surface defects on thin metallic wires10.1364/AO.39.000539 ↗
6. Deep learning10.1038/nature14539 ↗
7. Dumoulin, V., and Visin, F. (2016). A guide to convolution arithmetic for deep learning. arXiv.
8. Micro surface defect detection method for silicon steel strip based on saliency convex active contour model
9. Detection and classification of surface defects of gun barrels using computer vision and machine learning10.1016/j.measurement.2014.10.009 ↗
10. A fast detection method for region of defect on strip steel surface10.2355/isijinternational.55.207 ↗
11. Lin, T.Y., Dollár, P., Girshick, R., He, K.M., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
12. He, K.M., Zhang, X.Y., Ren, S.Q., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.
13. SegNet: A deep convolutional encoder-decoder architecture for image segmentation10.1109/TPAMI.2016.2644615 ↗
14. Long, J., Shelhamer, E., and Darrell, T. (2015, January 7–12). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.10.1109/CVPR.2015.7298965 ↗
15. Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention U-Net: Learning Where to Look for the Pancreas. arXiv.
16. Zhou, T., Fu, H., Chen, G., Shen, J., and Shao, L. (2020). Hi-net: Hybrid-fusion network for multi-modal MR image synthesis. IEEE Trans. Med. Imaging.10.1109/TMI.2020.2975344 ↗
17. Liu, Y., Cheng, M.M., Hu, X., Wang, K., and Bai, X. (2017, January 21–26). Richer convolutional features for edge detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.622 ↗
18. Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection10.1016/j.cirp.2016.04.072 ↗
19. Racki, D., Tomazevic, D., and Skocaj, D. (2018, January 12–15). A compact convolutional neural network for textured surface anomaly detection. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, CA, USA.10.1109/WACV.2018.00150 ↗
20. Mei, S., Wang, Y., and Wen, G. (2018). Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model. Sensors, 18.10.3390/s18041064 ↗
21. Segmentation-based deep-learning approach for surface-defect detection10.1007/s10845-019-01476-x ↗
22. Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5–9). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.10.1007/978-3-319-24574-4_28 ↗
23. Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8–14). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision, (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_49 ↗
24. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2–4). Tensorflow: A system for large-scale machine learning. Proceedings of the Symposium on Operating Systems Design and Implementation (OSDI), Savannah, GA, USA.
25. Ioffe, S., and Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv.
26. Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv.
27. Automated surface inspection of cold-formed micro-parts10.1016/j.cirp.2012.03.131 ↗
28. Timm, F., and Barth, E. (2011, January 25–27). Non-parametric texture defect detection using Weibull features. Proceedings of the SPIE—The International Society for Optical Engineering, San Francisco, CA, USA.10.1117/12.872463 ↗