Sewer Sediment Inspection Based on Multisensor Fusion Considering Sewage Flow
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-3626-7_34
Reference12 articles.
1. Zhou, Y.X., Ji, A.K., Zhang, L.M.: Sewer defect detection from 3D point clouds using a transformer-based deep learning model. Autom. Constr. 136, 12 (2022). https://doi.org/10.1016/j.autcon.2022.104163
2. Li, D.S., Cong, A.R., Guo, S.: Sewer damage detection from imbalanced CCTV inspection data using deep convolutional neural networks with hierarchical classification. Autom. Constr. 101, 199–208 (2019). https://doi.org/10.1016/j.autcon.2019.01.017
3. Wu, H.T., Huang, Y.R., Chen, L., Zhu, Y.J., Li, H.Z.: Shape optimization of egg-shaped sewer pipes based on the nondominated sorting genetic algorithm (NSGA-II). Environ. Res. 204, 10 (2022). https://doi.org/10.1016/j.envres.2021.111999
4. Liu, Z., Kleiner, Y.: State of the art review of inspection technologies for condition assessment of water pipes. Meas.: J. Int. Meas. Confed. 46(1), 1–15 (2013). https://doi.org/10.1016/j.measurement.2012.05.032
5. Dang, L.M., Kyeong, S., Li, Y., Wang, H., Nguyen, T.N., Moon, H.: Deep learning-based sewer defect classification for highly imbalanced dataset. Comput. Ind. Eng. 161 (2021). https://doi.org/10.1016/j.cie.2021.107630
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