INFRARED IMAGE HOT SPOT FAULT DETECTION FOR PHOTOVOLTAIC MODULES USING HCF-YOLO

Qin Yiming; Yin Liju; Gao Xiaoning; Wang Feng

doi:10.19912/j.0254-0096.tynxb.2024-1096

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (10) : 285-294. DOI: 10.19912/j.0254-0096.tynxb.2024-1096

INFRARED IMAGE HOT SPOT FAULT DETECTION FOR PHOTOVOLTAIC MODULES USING HCF-YOLO

Qin Yiming, Yin Liju, Gao Xiaoning, Wang Feng

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Abstract

When using drones to capture infrared images of photovoltaic modules for detection, the images often exhibit complex backgrounds, leading to challenges in detecting small target hot spots, which may result in information loss, false positives, or false negatives. In response to these issues, a novel fusion network（HCF-YOLO） is proposed by integrating the HCF-Net with the YOLOv8n network for detecting hot spot faults in infrared images of photovoltaic modules. This fusion network incorporates a Parallelized Patch-Aware Attention Module（PPA） to enhance the representation of small targets through hierarchical feature fusion and attention mechanisms, ensuring the retention of crucial hot spot information after multiple downsampling steps. Additionally, a Dimension-Aware Selective Integration Module（DASI） is employed to adaptively select and finely integrate high-dimensional and low-dimensional features, thereby enhancing the saliency of small targets. The Powerful IoU（PIOU） is adopted as the loss function for HCF-YOLO, guiding the early-stage regression of predicted boxes along effective paths to improve detection speed. The HCF-YOLO algorithm demonstrates a significant enhancement in detection accuracy, with the average precision at an intersection over union of 50%（AP50） increasing from 89.27% to 97.28% compared to the original YOLOv8n algorithm, achieving a detection speed of 217.33 frame/s. According to the experimental results, the effectiveness of the model is validated.

Key words

PV modules / infrared imaging / target detection / YOLOv8 / deep learning / attention mechanism

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Qin Yiming, Yin Liju, Gao Xiaoning, Wang Feng. INFRARED IMAGE HOT SPOT FAULT DETECTION FOR PHOTOVOLTAIC MODULES USING HCF-YOLO[J]. Acta Energiae Solaris Sinica. 2025, 46(10): 285-294 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1096

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