基于注意力机制的光伏组件热斑检测算法

热斑现象是造成光伏组件发电能力下降的重要原因之一，热斑检测是光伏电站运维必不可少的工作。然而分布式光伏电站的规模普遍较小、选址分散、环境复杂多样，使用传统的热斑检测算法需要投入大量的人力资源。基于此，提出了一种基于注意力机制的热斑检测算法HSNet。通过图像分割消除反光影响，结合通道注意力机制，学习通道间的特征信息，增强目标区域的重要性，采用自定义锚点的方法提高检测速度，使用焦点损失激活函数和基于物体先验概率的类别预测方式改善训练目标样本不均衡导致的分类准确性低的问题，通过回归方法获取准确的目标位置。实验表明:设计的目标检测算法在窗体回归精度和分类准确性方面均有明显的优势，边界框平均精度和准确率分别提升了3.18%和2.42%。 

Hot spot detection algorithm of photovoltaic module based on attention mechanism

The hot spot phenomenon is one of the important reasons for the reduction of power generation capacity of photovoltaic panels, and the detection of hot spots is an essential task for operation and maintenance personnel. The scale of distributed photovoltaic power plants is generally small, the site is scattered, the environment is complex and diverse, and the operation and maintenance personnel need to invest a lot of human resources to detect hot spots using traditional hot spot detection methods. In this paper, we propose a new hot spot detection algorithm HSNet. Firstly, the influence of reflection is eliminated through image segmentation. Secondly, the feature information between channels is learned in combination with the channel attention mechanism to enhance the importance of the target area. The method of user-defined anchor points is used to improve the detection speed. Then, the focus loss activation function and the category prediction method based on the prior probability of objects are used to improve the problem of low classification accuracy caused by the imbalance of training target samples, Finally, the accurate target position is obtained by regression method. Experiments show that the target detection algorithm designed in this paper has significant advantages over other algorithms in terms of window regression accuracy and classification accuracy, and the mean accuracy and accuracy of the bounding box are improved by 3.18% and 2.42%, respectively.