Amphibious detection system for drainage pipes base on deep learning

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.