利用无人机载热红外设备对光伏组件进行航拍和缺陷检测。针对无人机存储和算力的局限性,以及现有基于深度学习的缺陷检测模型在大型光伏电站复杂环境下模型参数量大和计算开销大的问题,设计使用YOLOv5 LiteX作为超轻量化的缺陷检测模型。首先,选择加权双向特征金字塔BiFPN替换原来的特征金字塔PANet,使特征有效的跨尺度连接和加权融合;其次,在特征融合的基础上增加更大的检测尺度,以提高模型检测较小缺陷目标的性能;引入focal-EIoU Loss对原有的边界框坐标预测损失加以改善,使网络专注于困难样本的运算。此外,通过数据增强方法来克服数据量过少的问题。改进后网络的平均精确率（mAP）相较于基准网络（Lite-YOLOv5）提高了7.32个百分点,困难样本（异物遮挡）的mAP大幅度提升。
Abstract
Unmanned aerial vehicle-borne thermal infrared equipment is used to carry out aerial photography and defect detection of solar photovoltaic modules. Aiming at the limitation of UAV’s memory and computing power, as well as the problems of the existing defect detection model based on deep learning in the complex environment of large photovoltaic power plants, such as large model parameters and large computational overhead, YOLOv5 LiteX is designed as an ultra-lightweight defect detection model. Firstly, the weighted bidirectional feature pyramid BiFPN is selected to replace the original feature pyramid PANet, so that the features can be effectively connected and fused across scales. Secondly, a larger detection scale is added on the basis of feature fusion to improve the performance of the model in detecting smaller defect targets. Focal-EIoU Loss is introduced to improve the prediction loss of the original frame coordinates so that the network could focus on the computation of difficult samples. In addition, the data enhancement method is used to overcome the problem of too little data. The mean average precision （mAP） of the improved network is 7.32 percentage points higher than that of the baseline network （Lite-YOLOv5）, and the mAP of difficult samples （foreign body occlusion） is greatly improved.
关键词
光伏组件 /
深度学习 /
目标检测 /
图像处理
Key words
photovoltaic modules /
deep learning /
object detection /
image processing
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基金
国家自然科学基金（61905172）; 山西省科技成果转化引导专项资助（202204021301059）; 山西省研究生教育创新（2020SY422）; 山西省重点研发计划项目（202102020101005）
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