基于主动激励红外成像的大坝渗漏AI识别方法研究

Study on dam leakage AI detection method based on infrared thermal imaging

为了解决大坝渗漏识别的问题,本文提出了一种将主动激励红外成像与深度学习结合的大坝渗漏识别方法。通过计算机仿真制作渗漏红外图像,再结合模拟大坝渗漏试验采集得到的红外图像,生成渗漏红外图像数据集用于深度学习的训练。在YOLOv5原始模型的基础上,用AF-FPN替换原有的FPN,提高识别大坝红外图像渗漏区域的能力,并在识别速度和准确率之间做出有效的权衡。试验表明,模型的准确率为87.6%,召回率为96.5%,平均准确率(IoU=0.5)为88.3%,表明本文提出的方法可较好的识别大坝红外图像渗漏区域。

In order to solve the problem of dam leakage detection,this paper presents a dam leakage detection method which combines active excitation infrared imaging with depth learning.The infrared image of leakage is produced by computer simulation,and then combined with the infrared image acquired by simulating dam leakage test,the infrared image data set is generated for the training of depth learning.On the basis of Yolov5 original model,using AF-FPN to replace FPN can improve the ability of identifying the leakage area of dam infrared image,and make an effective trade-off between identifying speed and accuracy.The test results show that the accuracy of the model is 87.6%,the recall rate is 96.5%,and the average accuracy(IoU=0.5)is 88.3%,which indicates that the method proposed in this paper can identify the leakage area of dam infrared image well.