Automatic recognition of earth rock embankment leakage based on UAV passive infrared thermography and deep learning
Automatic recognition of earth rock embankment leakage based on UAV passive infrared thermography and deep learning
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DOI：
10.1016/j.isprsjprs.2022.07.009
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发表时间：
2022-07-18
影响因子：
12.7
通讯作者：
Su, Huaizhi
中科院分区：
文献类型：
--
作者：
Zhou, Renlian;Wen, Zhiping;Su, Huaizhi
渗漏侵蚀是造成河堤决口的最有害的驱动因素之一，尤其是在汛期。但目前发现河堤渗漏的主要方式是人工巡查，严重阻碍了防灾工作。为实现路堤渗漏的高效检测和自动识别，本文首次提出了无人机被动红外热成像与迁移学习相结合的策略，作为确保路堤安全的创新方法。特别将堤防渗漏识别问题转化为图像分类问题。本研究的主要研究对象是边坡渗漏和管道，这是堤防破坏的两个最危险的原因。为了获得足够的模型训练图像，建立了一个能够模拟河堤实际使用条件下坡面渗漏和管道的露天仿真平台。在渗漏模拟平台上进行了500多次红外热成像实验，建立了包含1万多幅图像的红外图像数据库，其中包含6类堤防渗漏产生的各种热异常区域。利用这些图像和基于alexnet的迁移学习方法，训练出性能优异的图像分类模型。该模型在测试集上的分类准确率为94.90%，漏检率为0.64%，虚警率为2.65%。在模型部署前，采用t-SNE、Grad-CAM等可视化技术提供模型内部洞察，确保模型所关注的分类决策对象是合理的。最后，通过现场试验验证了无人机携带红外热像仪与该训练有素的模型相结合的可行性，表明所提出的泄漏检测与识别方法具有良好的适用性和泛化性。
Leakage erosion is one of the most harmful driving factors causing river embankment breaches, particularly in flood season. However, manual patrol is the main way to find river embankment leakage presently, which badly hinders disaster prevention. To realize the efficient detection and automatic identification of embankment leakage, for the first time the strategy of UAV carried passive infrared thermography combined with transfer learning is introduced herein as an innovative approach to ensure embankment safety. Especially, the problem of embankment leakage identification is transformed into image classification. The main research objects in this study are slope leakage and piping, two of the most dangerous causes of embankment failure. To obtain sufficient images for model training, an open-air simulation platform which can simulate the slope leakage and piping under the actual service conditions of river embankment is established. A total of more than 500 infrared thermography experiments are conducted on the leakage simulation platform and then an infrared image database containing more than 10,000 images which contain various thermal anomaly areas generated by 6 classes of embankment leakage is established. Using these images and AlexNet-based transfer learning method, an image classification model with excellent performance is trained. This model has a classification accuracy of 94.90%, a small leakage missed rate of 0.64%, and a small false alarm rate of 2.65% on the test set. Moreover, before model deployment, visualization techniques such as t-SNE and Grad-CAM are adopted to provide interior insight of the model to ensure that the objects of concern on which the model makes its classification decisions are reasonable. Finally, field tests demonstrated strong feasibility of UAV carried infrared thermography com-bined with this well-trained model, and revels that the proposed leakage detection and recognition approach has good applicability and generalization.