融入注意力机制的水位检测算法  

Water Level Detection Algorithm with Attention Mechanism Incorporated

作　　者：王坤侠 夏涛 WANG Kunxia;XIA Tao(School of Electronic and Information Engineering,Anhui Jianzhu University,Hefei 230601,China;Anhui International Joint Research Center for Ancient Architecture Intellisencing and Multi-Dimensional Modeling,Hefei 230601,China)

机构地区：[1]安徽建筑大学电子与信息工程学院,安徽合肥230601 [2]安徽省古建筑智能感知与高维建模国际联合研究中心,安徽合肥230601

出　　处：《湖南工业大学学报》2025年第1期57-63,共7页Journal of Hunan University of Technology

基　　金：安徽省住房城乡建设科学技术计划基金资助项目(2023-YF004,2023-YF113);安徽建筑大学智能建筑与建筑节能安徽省重点实验室开放课题基金资助项目(IBES2022ZR02)。

摘　　要：传统图像处理的水位检测算法易受环境因素的影响,因此提出一种融入注意力机制的水位检测算法。首先,利用融入CBAM注意力机制的YOLO v5目标检测模型获取尺身数字类别及其坐标信息;其次,通过融入ECA注意力机制的DeepLabv3+语义分割模型实现水尺和背景的分割;再次,使用边缘检测算法得出水位线信息;最后,根据尺身数字信息和水位线信息将像素值换算为真实水位值。实验结果表明,优化水位检测算法与人工读数误差在2 cm以内,满足水位检测标准要求。In view of the susceptibility of traditional image processing water level detection algorithms to environmental factors,a water level detection algorithm has thus been proposed with attention mechanism incorporated.Firstly,the YOLO v5 object detection model,with CBAM attention mechanism incorporated,is utilized to obtain the categorical classification of the water gauge and its corresponding coordinate information.Secondly,the segmentation of water gauge and background can be achieved through the DeepLabv3+semantic segmentation model with ECA attention mechanism incorporated.Then,an edge detection algorithms is applied to obtain water level information.Finally,the pixel values are converted to the true water level values based on the digital information of the ruler and the water level information.The experimental results show that the error between the optimized water level detection algorithm and manual reading is within 2 cm,which meets the requirements of water level detection standards.

关 键 词：水位检测 YOLO v5 DeepLabv3+ 注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]