针对传统红外热斑故障检测算法由于特征表达能力不佳造成算法易受复杂背景干扰以及对密集目标、小目标故障检测精度低的问题,提出一种基于高阶空间交互的光伏组件热斑故障检测网络。首先,设计高阶空间交互模块,并将其引入YOLOv5主干网络进行全局交互建模,提升网络对密集目标的检测精度;其次,为突出复杂背景下故障目标的关键特征,设计基于协同注意力的特征提取模块重构颈部网络;然后,在颈部网络设计多尺度特征增强融合模块以提高检测网络对不同尺度特征的充分利用;最后,设计自适应特征融合检测头以提高模型对小目标的感知能力。实验结果表明,相较于7种经典检测算法,所提出的算法检测精度最高,精度可达84.3%。
Abstract
Aiming at the problem of traditional infrared hot spot fault detection algorithm is prone to complex background interference and the low fault detection accuracy of small and dense targets, a photovoltaic module hot spot fault detection network based on high-order spatial interaction is proposed. Firstly, the high-order spatial interaction module is designed, and the YOLOv5 backbone network is introduced for global interaction modeling to improve the detection accuracy of the network. Secondly, in order to highlight the key features of fault targets in complex background, a CCA module is constructed based on the CA to reconstruct the neck network.Then, AFFM module in the neck network is designed to enhance the detection accuracy of the detection network of multi-scales. Finally,the self-adaptive feature fusion detection head is designed to improve the model’s perception of small targets. The experimental results suggest that compared with the seven classical detection algorithms, the proposed algorithm has the highest detection accuracy, reaching 84.3%.
关键词
光伏组件 /
故障检测 /
深度学习 /
热斑效应 /
高阶空间交互 /
特征融合
Key words
PV modules /
fault detection /
deep learning /
hot spot effect /
high-order space interaction /
feature fusion
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基金
国家自然科学基金（51804250）; 中国博士后科学基金（2020M683522）
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