Deep Architecture Based Spalling Severity Detection System Using Encoder-Decoder Networks

Author:

Yasmin Tamanna,Le Chuong,La Hung ManhORCID

Publisher

Springer Nature Switzerland

Reference27 articles.

1. Abdelkader, E.M., Moselhi, O., Marzouk, M., Zayed, T.: Evaluation of spalling in bridges using machine vision method. In: ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, vol. 37, pp. 1136–1143. IAARC Publications (2020)

2. Badrinarayanan, V., Handa, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. arXiv preprint arXiv:1505.07293 (2015)

3. Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)

4. Bai, M., Sezen, H.: Detecting cracks and spalling automatically in extreme events by end-to-end deep learning frameworks. In: ISPRS Annals of Photogrammetry and Remote Sensing Spatial Information Science, XXIV ISPRS Congress, International Society for Photogrammetry and Remote Sensing (2021)

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