基于FMF-YOLOv5的光伏组件红外图像故障诊断

doi:10.3778/j.issn.1002-8331.2308-0422

摘要/Abstract

摘要： 针对红外图像对比度较低、故障特征不明显的问题，提出全新的融合注意力机制（fusion attention mechanism，FAM），增强有效故障特征信息。创建新的融合金字塔池化（fusion spatial pyramid pooling，FSPP），增强特征提取能力。引入一种改进多层次融合卷积（multi-level fusion convolution，MFConv），利用MFConv构建的多层次跨阶段局部网络（multi-level cross stage partial network，MCSP）模块代替CSP模块，在提高少量模型参数量情况下，增加模型检测准确性。实验结果表明，在IoU阈值为0.5的情况下，该方法的平均精度（mAP）达到了93.1%。为光伏系统提供了可靠、高效的故障检测解决方案，从而使其成为提高系统性能和降低维护费用的实用解决方案。

关键词: 目标检测, 光伏故障, 特征融合, 融合注意力

Abstract: Aiming at the problems of low contrast of infrared image and not obvious fault characteristics, the paper firstly proposes a new fusion attention mechanism (FAM) to focus on important fault characteristics. Secondly, a new fusion spatial pyramid pooling (FSPP) is created to enhance feature extraction capabilities. Finally, a new multi-level fusion convolution (MFConv) is introduced, and a multi-level cross stage partial network is built by using MFConv. The MCSP module replaces the CSP module to maintain accuracy while increasing the number of model parameters with a small amount. The experimental results show that the average accuracy (mAP) of the proposed method reaches 93.1% when the IoU threshold is 0.5. This method provides a reliable and efficient fault detection solution for photovoltaic systems, making it a practical solution to improve system performance and reduce maintenance costs.

Key words: object detection, photovoltaic failure, feature fusion, fusion attention

张莉莉, 王修晖. 基于FMF-YOLOv5的光伏组件红外图像故障诊断[J]. 计算机工程与应用, 2025, 61(2): 327-334.

ZHANG Lili, WANG Xiuhui. Infrared Image Fault Diagnosis of Photovoltaic Modules Based on FMF-YOLOv5[J]. Computer Engineering and Applications, 2025, 61(2): 327-334.

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