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.

中文翻译：

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使用无人机热成像和基于 YOLO 的对象检测器自动定位堤坝泄漏出口


渗漏引起的土壤侵蚀对堤坝溃决构成重大威胁，尤其是在洪水期间。及时检测泄漏口并将其通知堤坝管理部门对于确保堤坝安全至关重要。然而，人工检查是目前识别泄漏口的主要方法，成本高昂、效率低下且缺乏空间覆盖。为了实现堤坝泄漏口的高效自动定位，提出了一种结合无人机、红外热成像和深度学习的创新策略。无人机用于堤坝的表面传感。来自这些无人机的实时图像被发送到服务器，在那里部署了训练有素的探测器。一旦检测到泄漏出口，就会远程向堤坝管理人员发送警报信息。为了实现这一策略，使用了 4 个热像仪对几个型号的泄漏出口和实际堤坝进行成像。选择了 9,231 张手动标记的热图像和 13,387 个泄漏物体进行分析。19 个检测器使用迁移学习进行训练。最好的检测器在具有挑战性的测试集中实现了 95.8% 的平均精度。建造了全尺寸路堤用于泄漏出口检测测试。各种现场测试证实了所提出的泄漏出口定位方法的有效性。在一些恶劣的条件下，训练有素的探测器显然也优于人工判断。结果表明，在典型情况下，所提方法的定位误差在5 m以内，证明了其实际可靠性。最后，彻底检查了所建议策略的影响因素和局限性。