Abstract
As the demand for tunnel inspection grows, visual methods are being utilized, but traditional machine learning techniques struggle with continuous learning and updating prototype classes without repeated defect data acquisition. Incremental learning provides a way to address catastrophic forgetting, but conventional methods often cannot handle the imbalanced data typical in real-world situations. This paper proposes a two-stage learning paradigm specifically designed for incremental learning to reduce overfitting from head data in unbalanced classes. Our approach maximizes symmetric separation of inter-class prototypes in the classifier space by integrating DR loss within the ETF classifier. Compared to other leading incremental learning methods, our approach shows superior performance on tunneling data.
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No datasets were generated or analysed during the current study.
References
Xiong, L., Zhang, D., Zhang, Y.: Water leakage image recognition of shield tunnel via learning deep feature representation. J. Vis. Commun Image Represent. 71, 102708 (2020)
He, K., Gkioxari, G., Dollár, P., Girshick, R. (2017) : Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969
French, R.M., Chater, N.: Using noise to compute error surfaces in connectionist networks: a novel means of reducing catastrophic forgetting. Neural Computation 14(7), 1755–1769 (2002)
Grossberg, S.: Adaptive resonance theory: how a brain learns to consciously attend, learn, and recognize a changing world. Neural networks 37, 1–47 (2013)
Shi, Y., Zhou, K., Liang, J., Jiang, Z., Feng, J., Torr, P.H., Bai, S., Tan, V.Y. (2022): Mimicking the oracle: An initial phase decorrelation approach for class incremental learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16722–16731
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A. (2017) : Overcoming catastrophic forgetting in neural networks. In: Proceedings of the national academy of sciences 114(13), 3521–3526
Qiu, Z., Xu, L., Wang, Z., Wu, Q., Meng, F., Li, H.: Ism-net: mining incremental semantics for class incremental learning. Neurocomputing 523, 130–143 (2023)
Douillard, A., Cord, M., Ollion, C., Robert, T., Valle, E. (2020): Podnet: Pooled outputs distillation for small-tasks incremental learning. In: Computer vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XX 16, pp. 86–102 . Springer
Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y. (2019) : Large scale incremental learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 374–382
Wang, F.-Y., Zhou, D.-W., Ye, H.-J., Zhan, D.-C. (2022): Foster: Feature boosting and compression for class-incremental learning. In: European Conference on Computer Vision, pp. 398–414. Springer
Wallace, B.C., Small, K., Brodley, C.E., Trikalinos, T.A. (2011): Class imbalance, redux. In: 2011 IEEE 11th International Conference on Data Mining, pp. 754–763 . Ieee
He, C., Wang, R., Chen, X.(2021) : A tale of two cils: The connections between class incremental learning and class imbalanced learning, and beyond. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3559–3569
Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural networks 106, 249–259 (2018)
Yang, Y., Yuan, H., Li, X., Lin, Z., Torr, P., Tao, D. (2023): Neural collapse inspired feature-classifier alignment for few-shot class incremental learning. arXiv preprint arXiv:2302.03004
Fontana, M., Zeni, G., Vantini, S.: Conformal prediction: a unified review of theory and new challenges. Bernoulli 29(1), 1–23 (2023)
Liang, T.(2008) : Investigation and analysis of tunnel diseases and risk analysis and control of lining structures. PhD thesis, Hangzhou,Zhejiang University
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., Larochelle, H.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H. (2017): icarl: Incremental classifier and representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2001–2010
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Cai wrote the main manuscript text and figures. Gao and Yang instructed the experiment operation. Feng help check and revise the mistakes in language and grammar. All authors reviewed the manuscript.
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Cai, Y., Gao, X., Yang, Y. et al. U2bil:a two-phase class separation method for unbalanced tunnel defects via class incremental learning. SIViP 19, 151 (2025). https://doi.org/10.1007/s11760-024-03651-x
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DOI: https://doi.org/10.1007/s11760-024-03651-x