光伏组件表面钢化玻璃会导致采集的热红外图像中带有反光噪声, 其与热斑的特征相似,热斑检测中常出现误检。该文提出一种多尺度融合注意力机制的轻量化DeepLabv3+语义分割模型LD-MA（lightweight DeepLabv3+ with multi-scale integrated attention mechanism）用于热斑检测。LD-MA基于DeepLabv3+网络架构,首先引用MobileNetV2作为主干特征提取网络,减小网络参数量以提高训练效率。然后设计多尺度特征融合模块并引入CBAM注意力机制,保留多阶段目标特征且强化对热斑目标特征信息和位置信息的学习。在自建光伏热斑数据集进行热斑检测实验,结果表明LD-MA模型参数量大幅减少,同时有效避免误检和漏检,在测试集中平均交并比（mIoU）和类别平均像素准确率（mPA）分别达到90.82%和94.39%。
Abstract
The tempered glass on the surface of photovoltaic modules will cause reflection noise in the collected thermal infrared images, which is similar to the characteristics of hot spots, which will often leads false detections in hot spot detection task. This paper proposes a lightweight DeepLabv3+ semantic segmentation model called LD-MA （Lightweight DeepLabv3+ with Multi-scale integrated Attention Mechanism） for hot spot detection. LD-MA is based on the DeepLabv3+ network architecture, First, MobileNetV2 is used as the backbone feature extraction network to reduce the amount of network parameters to improve training efficiency. Then, a multi-scale feature fusion module is designed and a CBAM attention mechanism is introduced to retain the multi-stage target features and strengthen the learning of hot spot target feature information and location information. The hot spot detection experiment was carried out on the self-built photovoltaic hot spot data set, and the results showed that the parameters of the LD-MA model were greatly reduced, and at the same time, false detection and missed detection were effectively avoided. In the test set, mIoU and mPA reached 90.82% and 94.39%.
关键词
光伏组件 /
故障检测 /
语义分割 /
热斑 /
反光噪声
Key words
PV modules /
fault detection /
semantic segmentation /
hot spot /
reflection noise
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基金
上海市自然科学基金面上项目（20ZR1421300）; 上海市浦江（D类）人才计划（21PJD025）; 上海市科委创新行动科技支撑碳达峰碳 中和（21DZ1207300）; 国家科技部外国专家局项目（DL2022013007L)
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