摘要
利用无人机对光伏组件进行故障巡检通常从可见光和红外光两种场景分别处理和检测。该文提出基于残差神经网络ResNet50和改进的YOLOv5故障检测方法,实现对两种影像图像高精度自动分类和故障检测。针对红外数据进行色度变换去除太阳反光而保留热斑,针对可见光数据采用锐化的方式凸显异物、裂痕等小目标,使用不同的YOLOv5目标检测算法实现可见光下小型异物故障和红外光下热斑故障的快速检测和定位。
The inspection of photovoltaic modules for faults using drones is typically conducted by processing and detecting in both visible light and infrared light scenarios separately.This paper proposes a fault detection method based on the residual neural network ResNet50 and improved YOLOv5,achieving high-precision automatic classification and fault detection of two types of image.For infrared data,chromaticity transformation is used to remove sun reflection and retain hot spots,while for visible light data,sharpening is used to highlight small targets such as foreign objects and cracks.Different YOLOv5 object detection algorithms are used to achieve fast detection and positioning of small foreign object faults under visible light and hot spot faults under infrared light.
作者
范钧玮
饶全瑞
赵薇
宋美
刘广臣
Fan Junwei;Rao Quanrui;Zhao Wei;Song Mei;Liu Guangchen(School of Information and Electrical Engineering,Ludong University,Yantai 264025,China;School of Mathematics and Statistical Science,Ludong University,Yantai 264025,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2024年第7期510-516,共7页
Acta Energiae Solaris Sinica
基金
国家级大学生创新创业训练项目(202310451208)
山东省大学生创新训练项目(S202210451041)
山东省高等学校教学研究与改革面上项目(M2018X066)
鲁东大学“专创融合”课程建设重点项目(2021Z08)。
