IMPROVED YOLOv5 DUAL-IMAGE PHOTOVOLTAIC FAULT SMALL TARGET DETECTION

Fan Junwei; Rao Quanrui; Zhao Wei; Song Mei; Liu Guangchen

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

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (7) : 510-516. DOI: 10.19912/j.0254-0096.tynxb.2023-0462

IMPROVED YOLOv5 DUAL-IMAGE PHOTOVOLTAIC FAULT SMALL TARGET DETECTION

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

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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.

Key words

photovoltaic modules / deep learning / objection detection / ResNet50 / YOLOv5

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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

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