Amphibious detection system for drainage pipes base on deep learning

Pengfei Yong

doi:10.1117/12.2658645

22 December 2022 Amphibious detection system for drainage pipes base on deep learning

Pengfei Yong

Proceedings Volume 12460, International Conference on Smart Transportation and City Engineering (STCE 2022); 124602H (2022) https://doi.org/10.1117/12.2658645
Event: International Conference on Smart Transportation and City Engineering (STCE 2022), 2022, Chongqing, China

Abstract

The defect of underground drainage pipes is the main inducing factor of urban disasters. However, existing detection robot has problems such as poor environmental adaptability and a low degree of automation for pipes. The deep learning-based amphibious robot designed in this study is a highly adaptable and efficient detection system. The designed ducted screw propelled wheels first provide power. Next, based on the multimodal sensors and the improved YOLOV4-Tiny, defect detection and 3D reconstruction are carried out. Finally, the defect location and image information are transmitted to the terminal for display by wire, and a detection report is generated. What’s more, the experimental results show that the MAP of the improved YOLOV4-Tiny in this research is improved by 2.18% compared with the baseline network, and the FPS is improved by 11.3 frames. The system proposed provides a new approach to drainage pipe inspection.

Citation Download Citation

Pengfei Yong "Amphibious detection system for drainage pipes base on deep learning", Proc. SPIE 12460, International Conference on Smart Transportation and City Engineering (STCE 2022), 124602H (22 December 2022); https://doi.org/10.1117/12.2658645

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