PHOTOVOLTAIC HOTSPOT DEFECT DETECTION BASED ON CROSS-LEVEL LOCAL FEATURE FUSION

Xiang Xinjian; Sun Siqi; Luo Qiuxia; Tang Xiaohui; Chen Zhenli; Zheng Yongping

doi:10.19912/j.0254-0096.tynxb.2024-2032

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (3) : 732-738. DOI: 10.19912/j.0254-0096.tynxb.2024-2032

PHOTOVOLTAIC HOTSPOT DEFECT DETECTION BASED ON CROSS-LEVEL LOCAL FEATURE FUSION

Xiang Xinjian¹, Sun Siqi¹, Luo Qiuxia², Tang Xiaohui², Chen Zhenli², Zheng Yongping¹

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Abstract

To address the issue of missed detections of small hotspots caused by the loss of detailed features in photovoltaic hotspot defect detection, this paper proposes an improved method for cross-level local feature fusion based on YOLOv8. The method introduces the BiFormer concept into the YOLOv8 backbone to enhance the model’s ability to extract detailed information. It replaces traditional upsampling methods with a Dysample dynamic upsampling module, better preserving the details of high-resolution feature maps. Additionally, an FCLAHead detection head is designed for cross-level local feature fusion, establishing feature associations across different feature map levels to achieve information complementarity. This fusion operation enhances the feature representation of small hotspots, improving the model’s detection capability for small hotspots across various scenarios. Compared to the base model YOLOv8n, the improved model increases mean average precision （mAP） from 86.3% to 90%, with precision and recall improving by 1.1 and 3.1 percentage points, respectively. The model’s parameter count is only 3.08×10⁶, with a slight increase in computation, making it suitable for applications requiring high accuracy in hardware-limited environments.

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photovoltaic effects / object detection / deep learning / hot spot / YOLOv8 / infrared imaging / feature extraction

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Xiang Xinjian, Sun Siqi, Luo Qiuxia, Tang Xiaohui, Chen Zhenli, Zheng Yongping. PHOTOVOLTAIC HOTSPOT DEFECT DETECTION BASED ON CROSS-LEVEL LOCAL FEATURE FUSION[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 732-738 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2032

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