摘要
随着光伏产业的快速发展,快速检测光伏板的异常已经成为提升巡检效率的关键要素。然而,传统的光伏板缺陷检测方法存在效率不高、检测效果差等问题。本文提出了一种基于红外热成像的光伏发电板缺陷检测技术,分析了红外检测技术用于光伏板缺陷检测的原理,通过引入SENet注意力到YOLOV4网络,完成了光伏发电板缺陷的智能检测。
With the rapid development of the photovoltaic industry,rapid detection of abnormalities in photovoltaic panels has become a key element in improving inspection efficiency.However,traditional defect detection methods for photovoltaic panels have problems such as low efficiency and poor detection results.This article proposes a photovoltaic panel defect detection technology based on infrared thermal imaging,analyzes the principle of infrared detection technology for photovoltaic panel defect detection,and achieves intelligent detection of photovoltaic panel defects by introducing SENet attention to YOLOV4 network,
作者
何心坤
He Xinkun(Shandong Shengwo Plastic Machinery Technology Co.,Ltd.,Jinan,Shandong 271100,China)
出处
《云南电力技术》
2024年第2期74-76,共3页
Yunnan Electric Power
关键词
深度学习
缺陷检测
光伏发电
注意力机制
Deep learning
defect detection
photovoltaic power generation
attention mechanism