Automated localization of dike leakage outlets using UAV-borne thermography and YOLO-based object detectors

Leakage-induced soil erosion poses a major threat to dike failure, particularly during floods. Timely detection and notification of leakage outlets to dike management are crucial for ensuring dike safety. However, manual inspection, the current main approach for identifying leakage outlets, is costly, inefficient, and lacks spatial coverage. To achieve efficient and automatic localization of dike leakage outlets, an innovative strategy combining drones, infrared thermography, and deep learning is presented. Drones are employed for dikes’ surface sensing. Real-time images from these drones are sent to a server where well-trained detectors are deployed. Once a leakage outlet is detected, alarming information is remotely sent to dike managers. To realize this strategy, 4 thermal imagers were employed to image leaking outlets of several models and actual dikes. 9,231 hand-labeled thermal images with 13,387 leaking objects were selected for analysis. 19 detectors were trained using transfer learning. The best detector achieved a mean average precision of 95.8 % on the challenging test set. A full-scale embankment was constructed for leakage outlet detection tests. Various field tests confirmed the efficiency of the proposed leakage outlet localization method. In some tough conditions, the trained detector also evidently outperformed manual judgement. Results indicate that under typical circumstances, the localization error of the proposed method is within 5 m, demonstrating its practical reliability. Finally, the influencing factors and limits of the suggested strategy are thoroughly examined.