一种基于航拍红外图像的光伏热斑故障分类检测方法

张妍; 裴兴豪; 李冰; 张雄

doi:10.19912/j.0254-0096.tynxb.2023-0762

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (9) : 353-359. DOI: 10.19912/j.0254-0096.tynxb.2023-0762

一种基于航拍红外图像的光伏热斑故障分类检测方法

张妍^1,2, 裴兴豪^1,2, 李冰¹, 张雄¹

作者信息 +

CLASSIFICATION DETECTION METHOD OF PHOTOVOLTAIC THERMAL SPOT IN AERIAL INFRARED IMAGE

Zhang Yan^1,2, Pei Xinghao^1,2, Li Bing¹, Zhang Xiong¹

Author information +

文章历史 +

摘要

针对航拍光伏红外图像热斑检测方法中小目标特征易丢失问题,提出一种光伏热斑故障分类检测方法。首先将多头自注意力机制结合CSPNet结构进行改进,提出CSPMAT网络,再将其引入New CSP-Darknet网络,构建CSPMAT-Darknet模型,实现了光伏组件热斑定位及分类。实验结果表明：该模型在小目标检测任务中的性能显著提升,且在目标尺寸差异较大的故障分类检测任务中,均值平均精度达到82.92%,提高了13.97个百分点,具有良好的检测精度和泛化能力。

Abstract

A photovoltaic thermal spot fault classification detection method is proposed to resolve the issue of small target feature loss in the thermal spot detection method for aerial photovoltaic infrared images. Firstly, the multi-head self-attention mechanism is integrated with the CSPNet structure for improvement, resulting in the proposed CSPMAT network. Subsequently, this network is introduced into the New CSP-Darknet architecture, leading to the construction of the CSPMAT-Darknet model, achieving both localization and classification of photovoltaic component thermal spots. Experimental results demonstrate that the model enhances performance in small target detection tasks significantly. Moreover, in fault classification detection tasks with substantial target size variations, the achieved mean average precision （mAP） reaches 82.92%, an increase of 13.97 percentage points, thereby showcasing commendable detection accuracy and generalization capability.

导出引用

张妍, 裴兴豪, 李冰, 张雄. 一种基于航拍红外图像的光伏热斑故障分类检测方法[J]. 太阳能学报. 2024, 45(9): 353-359 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0762

Zhang Yan, Pei Xinghao, Li Bing, Zhang Xiong. CLASSIFICATION DETECTION METHOD OF PHOTOVOLTAIC THERMAL SPOT IN AERIAL INFRARED IMAGE[J]. Acta Energiae Solaris Sinica. 2024, 45(9): 353-359 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0762

中图分类号： TP18 TM615

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