南昌工程学院学报2024，Vol.43Issue(3)：13-18,6.
基于改进YOLOX的城市河道智能水位测量算法
Intelligent water level measurement algorithm for urban rivers based on improved YOLOX
摘要
Abstract
In response to the problem of insufficient feature information extraction in current deep learning based water level measurement algorithms,an intelligent water level measurement algorithm for urban rivers based on improved YOLOX is proposed.To improve the recognition rate of YOLOX for multi-class dense targets,CBAM attention mechanism is introduced in the feature fusion network,and a loss function D-IoU based on calculating target box information is adopted to accelerate the convergence of the model.This algorithm uses the improved YOLOX to identify and statistically analyze the scales and numbers on the water gauge,and calculate the water level value.The experiment shows that the proposed method has an av-erage recognition rate of 98.62%and 92.23%for water level scale and number,respectively.The final average error in cal-culating water level is 1.16cm,which is 1.76cm less than the average error of other image recognition water level measure-ment algorithms.It can achieve high-precision intelligent measurement of water level values in urban rivers.关键词
深度学习/水位测量/CBAM/DIoUKey words
deep learning/water level measurement/CBAM/DIoU分类
信息技术与安全科学引用本文复制引用
吕姚,包学才,彭宇,查小红,黄明坤..基于改进YOLOX的城市河道智能水位测量算法[J].南昌工程学院学报,2024,43(3):13-18,6.基金项目
江西省水利厅科技项目(编号202223YBKT24) （编号202223YBKT24）
