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
吕姚 1包学才 1彭宇 1查小红 2黄明坤1
作者信息
- 1. 南昌工程学院信息工程学院;江西省水信息协同感知与智能处理重点实验室
- 2. 南昌工程学院网络信息中心,江西南昌 330099
- 折叠
摘要
针对目前基于深度学习水位测量算法存在特征信息提取不充分问题,提出一种基于改进YOLOX的城市河道水位智能测量算法.为了提高YOLOX对多类别密集目标的识别率,在特征融合网络中引入CBAM注意力机制,并采用基于计算目标框信息的损失函数D-IoU加快模型收敛.该算法利用改进后的YOLOX对水尺刻度进行识别与统计,并计算出水位值.试验表明提出的新算法对水尺刻度和数字的平均识别率分别达 98.62%和 92.23%,最终计算水位的平均误差为1.16 cm,较其他图像识别水位测量算法的平均误差减少了 1.76 cm,可实现高精度智能测量城市河道的水位值.
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引用本文复制引用
基金项目
江西省水利厅科技项目(202223YBKT24)
出版年
2024
国家科技期刊平台
NSTL
万方数据