改进的YOLOv5双影像光伏故障小目标检测

范钧玮; 饶全瑞; 赵薇; 宋美; 刘广臣

doi:10.19912/j.0254-0096.tynxb.2023-0462

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (7) : 510-516. DOI: 10.19912/j.0254-0096.tynxb.2023-0462

改进的YOLOv5双影像光伏故障小目标检测

范钧玮¹, 饶全瑞¹, 赵薇², 宋美², 刘广臣²

作者信息 +

IMPROVED YOLOv5 DUAL-IMAGE PHOTOVOLTAIC FAULT SMALL TARGET DETECTION

Fan Junwei¹, Rao Quanrui¹, Zhao Wei², Song Mei², Liu Guangchen²

Author information +

文章历史 +

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.

导出引用

范钧玮, 饶全瑞, 赵薇, 宋美, 刘广臣. 改进的YOLOv5双影像光伏故障小目标检测[J]. 太阳能学报. 2024, 45(7): 510-516 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0462

Fan Junwei, Rao Quanrui, Zhao Wei, Song Mei, Liu Guangchen. IMPROVED YOLOv5 DUAL-IMAGE PHOTOVOLTAIC FAULT SMALL TARGET DETECTION[J]. Acta Energiae Solaris Sinica. 2024, 45(7): 510-516 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0462

中图分类号： TP391 TP18

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