摘要
光伏板的清洁与维护是光伏电站运行中的关键任务,其中搭载机械臂和光伏清洁终端的光伏清洁摆渡车系统成为一种创新的解决方案.这类系统需要精确获取光伏板的位姿,包括车身相对光伏板的倾角与距离.为解决这一问题,本文提出了一种基于单目相机的光伏板视觉定位方法.本文首先对YOLOv8-pose关键点检测模型进行改进以提高检测精度.引入PSA注意力机制优化骨干网络,采用DySample动态上采样模块并提出ADown*下采样模块对颈部网络进行增强.然后结合改进后的YOLOv8-pose与光伏板的几何特征,提出倾角和距离计算方法,从而实现光伏板的姿态定位.实验结果表明,本文提出的改进算法相较于原始YOLOv8-pose计算的光伏板倾角与距离精度分别提升了26.2%与20.1%,能够更准确地对光伏板进行定位.
The cleaning and maintenance of photovoltaic(PV)panels are critical tasks in the operation of PV power stations.In this context,the PV cleaning shuttle systems equipped with robotic arms and PV cleaning terminals have emerged as an innovative solution.These systems require precise acquisition of PV panel poses,including the tilt angle and distance relative to the vehicle body.To address this issue,this study proposes a monocular-camera-based visual positioning method for PV panels.First,the YOLOv8-pose keypoint detection model is improved to enhance detection accuracy.The PSA mechanism is introduced to optimize the backbone network,while the DySample dynamic upsampling module and proposed ADown*downsampling module are used to strengthen the neck network.Next,by combining the improved YOLOv8-pose with the geometric features of the PV panels,a method for calculating the tilt angle and distance is proposed,thereby achieving the pose positioning of PV panels.Experimental results show that the proposed improved algorithm achieves a 26.2%and 20.1%increase in the accuracy of calculating tilt angles and distances compared to the original YOLOv8-pose,enabling more precise positioning of PV panels.
作者
蒋天玮
朱明
JIANG Tian-Wei;ZHU Ming(Department of Automation,School of Information Science and Technology,University of Science and Technology of China,Hefei 230026,China)
出处
《计算机系统应用》
2025年第7期14-22,共9页
Computer Systems & Applications
基金
科技创新特区计划(20-163-14-LZ-001-004-01)。
