Abstract
There are various characteristics of industrial defects, and there is no fixed pattern to search for. Typically, anomaly detection models are used to identify defects. However, after experiencing a domain gap, industrial defect images often lead to a decrease in the verification accuracy of the source model. We conducted experiments to validate this and employed a domain adaptation model. Using color transformation algorithms, we generated source images with domain gaps and introduced them to a pre-trained model. We trained the model to learn features from the source domain and utilized a domain discriminator to differentiate between features from the source and target domains, assuming that the mappings of the target and source domains come from the same distribution. Comparative experimental results demonstrate that the domain adaptation model has a significant impact on improving accuracy. Specifically, the accuracy of the original “flower” category increased from 43.98% to 89.23%, and the “cable” category improved from 75.33% to 85.66%.
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References
Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., van den Hengel, A.: Memorizing normality to detect anomaly: Memory-augmented deep auto encoder for unsupervised anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1705–1714 (2019)
Ristea, N.-C., Madan, N., Ionescu, R.T., Nasrollahi, K., Shahbaz Khan, F., Moeslund, T.B., Shah, M.: Self-supervised predictive convolutional attentive block for anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13576–13586 (2022)
Li, C.-L., Sohn, K., Yoon, J., Pfister, T.: Cutpaste: Self-supervised learning for anomaly detection and localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9664–9674 (2021)
Zavrtanik, V., Kristan, M., Skocaj, D.: Draem-a discriminatively trained reconstruction embedding for surface anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8330–8339 (2021)
Defard, T., Setkov, A., Loesch, A., Audigier, R.: Padim: a patch distribution modeling framework for anomaly detection and localization. In: Del Bimbo, A., et al. (eds.) Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part IV, pp. 475–489. Springer International Publishing, Cham (2021)
Roth, K., Pemula, L., Zepeda, J., Scholkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318–14328 (2022)
De, K., Pedersen, M.: Impact of colour on robustness of deep neural networks. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada, 2021, pp. 21–30. https://doi.org/10.1109/ICCVW54120.2021.00009
Yang, M., Wu, P., Feng, H.: MemSeg: a semi-supervised method for image surface defect detection using differences and commonalities. Eng. Appl. Artif. Intell. 119, 105835 (2023)
Liu, Z., Li, C.-L., Sohn, K., Yoon, J., Pfister, T.: SimpleNet: a simple network for image anomaly detection and localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
Ganin, Y., Lempitsky, V.: Unsupervised Domain Adaptation by Backpropagation. In: International Conference on Machine Learning (ICML), pp. 1180–1189 (2015)
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Computer Vision and Pattern Recognition (CVPR) (2017)
Acknowledgment
The authors would like to thank the National Science and Technology Council, Taiwan, R. O. C. for financially supporting this research under Contract No. NSTC 112-2221-E-240-002-MY3.
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Wu, C.H., Cheng, R.S., Chen, C.H. (2024). Study on Anomaly Classifier with Domain Adaptation. In: Deng, DJ., Chen, JC. (eds) Smart Grid and Internet of Things. SGIoT 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-031-55976-1_1
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DOI: https://doi.org/10.1007/978-3-031-55976-1_1
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