基于YOLOv7-EPAN的光伏板红外图像缺陷检测

Defect Detection of Photovoltaic Panel Infrared Image Based on YOLOv7-EPAN

光伏板是光伏电站重要组成部件,需定期对其进行检测,保证光伏电站安全运行。针对航拍光伏图像复杂背景下小目标难检测的问题,提出一种基于YOLOv7-EPAN的光伏板红外图像缺陷检测方法。首先提出融合CSWin Transformer的扩展高效网络CS-ELAN模块,捕获全局有效信息抑制背景信息;其次以CS-ELAN为基础构建高效路径特征聚合网络EPAN(Efficient path aggregation characteristic pyramid network),加强不同特征层的信息交互,丰富语义特征信息,提高特征表达能力;最后优化损失函数,使模型关注高质量先验框,提高小目标定位精度。在航拍光伏红外数据集上进行实验,结果表明:相比于原YOLOv7模型,所提方法的mAP50、mAP50:95分别提高了6.4%、3.3%,表明所提方法能较好地解决航拍光伏图像复杂背景下小目标缺陷漏检的问题。

Photovoltaic(PV)panels are an important component of photovoltaic power stations.They must be tested regularly to ensure a safe operation of the photovoltaic power station.To address the problem of small targets being difficult to detect among the complex background of aerial photovoltaic images,a defect detection method based on YOLOv7-EPAN for infrared photovoltaic panel images is proposed.First,an extended efficient network CS-ELAN module integrated with a CSWin Transformer is proposed for capturing global information effectively and suppressing background information.Second,an efficient path aggregation characteristic pyramid network(EPAN)is constructed based on CS-ELAN to enhance the information interaction between different feature layers,enrich the semantic feature information,and improve the feature expression ability.Finally,the loss function is optimized to focus the model on a prior high-quality frame and improve the positioning accuracy of small targets.The experimental results show that compared with the original YOLOv7 model,the mAP50 and mAP50:95 of the proposed method show an improvement of 6.4%and 3.3%,respectively,indicating that the proposed method can better solve the problem of missing small target defects among the complex background of aerial photovoltaic images.