水利水电科技进展2024，Vol.44Issue(1)：89-94,6.DOI:10.3880/j.issn.1006-7647.2024.01.013
基于YOLO模型的堤坝管涌监测智能识别方法
Intelligent identification method of dyke piping monitoring based on YOLO model
摘要
Abstract
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.关键词
堤坝管涌/感兴趣区域/YOLO v3模型/多权值神经网络Key words
dyke piping/region of interest/YOLO v3 model/multi-weight neural network分类
信息技术与安全科学引用本文复制引用
陆公义,欧阳鹏,程赟,羌予践,华亮..基于YOLO模型的堤坝管涌监测智能识别方法[J].水利水电科技进展,2024,44(1):89-94,6.基金项目
南通市市级社会民生科技重点项目(MS22021032) （MS22021032）
