DETECTION OF WEAK PHOTOVOLTAIC ARRAY HOTSPOTS IN UAV AERIAL INFRARED IMAGES BASED ON YOLOv5

Peng Ziran; Wang Siyuan; Zhang Yingqing; Xiao Shenping

doi:10.19912/j.0254-0096.tynxb.2024-1304

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 315-323. DOI: 10.19912/j.0254-0096.tynxb.2024-1304

DETECTION OF WEAK PHOTOVOLTAIC ARRAY HOTSPOTS IN UAV AERIAL INFRARED IMAGES BASED ON YOLOv5

Peng Ziran^1,2, Wang Siyuan^1,2, Zhang Yingqing^1,2, Xiao Shenping^1,2

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Abstract

To address the challenges of weak hotspot signals and blurred features in infrared images of photovoltaic modules captured by drones, as well as the high miss and false detection rates in automated inspections, this paper presents an optimized version of the YOLOv5 deep learning model to enhance its ability to detect faint infrared hotspots. First, by incorporating a hybrid feature module （HFM）, the network's feature extraction capability is further enhanced, leading to a significant improvement in detection accuracy. Additionally, a hierarchical aggregation Injection （HAI） network is employed after the backbone of YOLOv5, enabling the convolutional kernels to capture global feature information, thereby enforcing high detection precision. Finally, the WIoU loss function is introduced to improve the original bounding box loss and increase the detection speed of difficult samples. The experimental results show that the mAP of the improved model reaches 80.3%, which is an improvement of 5 percentage points compared to the original model.

Key words

photovoltaic modules / deep learning / object detection / image processing / UAV inspection / infrared thermal imaging

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Peng Ziran, Wang Siyuan, Zhang Yingqing, Xiao Shenping. DETECTION OF WEAK PHOTOVOLTAIC ARRAY HOTSPOTS IN UAV AERIAL INFRARED IMAGES BASED ON YOLOv5[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 315-323 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1304

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