基于YOLO模型的堤坝管涌监测智能识别方法

针对堤坝管涌现象的监测识别问题,提出一种基于YOLO模型的堤坝管涌识别方法。该方法通过引入改进的残差块及替换模型的激活函数来提升YOLO v3模型的网络性能,构建了基于堤坝管涌感兴趣区域提取的Piping YOLO模型来提取管涌感兴趣区域,并采用二维主成分分析方法提取管涌现象的特征,将其作为多权值神经网络的输入,经训练后实现管涌状态的分类识别。基于自主搭建的管涌渗漏试验平台建立了数据集并进行了试验验证,结果表明,提出的方法能有效识别堤坝管涌现象,在堤坝管涌无人巡检领域具有一定的应用前景。

Intelligent identification method of dyke piping monitoring based on YOLO model

Aiming at the problem of monitoring and identification of dike piping phenomenon, a method for dike piping identification based on the YOLO model was proposed. A Piping YOLO model based on region of interest (ROI) extraction was proposed to improve the performance of YOLO v3 network by introducing improved residual block and activation function of replacement model. After the ROI was extracted, the two-dimensional principal component analysis method was used to extract the characteristics of piping phenomenon, which was used as the input of multi-weight neural network to realize the classification and recognition of piping state through training. The experimental platform of dike piping was built, and the data set was established to verify the effectiveness of the proposed method. The results show that the proposed method can effectively identify the phenomenon of dike piping, and has a certain application prospect in the field of unmanned inspection of dike piping.