改进的YOLOv5双影像光伏故障小目标检测
IMPROVED YOLOv5 DUAL-IMAGE PHOTOVOLTAIC FAULT SMALL TARGET DETECTION
范钧玮 1饶全瑞 1赵薇 2宋美 2刘广臣2
作者信息
- 1. 鲁东大学信息与电气工程学院,烟台 264025
- 2. 鲁东大学数学与统计科学学院,烟台 264025
- 折叠
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.
Abstract
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.
关键词
光伏组件/深度学习/目标检测/ResNet50/YOLOv5Key words
photovoltaic modules/deep learning/objection detection/ResNet50/YOLOv5引用本文复制引用
基金项目
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)
出版年
2024
NETL
NSTL
万方数据