基于语义分割的光伏组件积灰检测与分析

为了解决恶略环境条件下难以对光伏电池板表面积灰定性定量分析的问题，提出了一种基于深度学习算法的光伏电池板表面积灰智能检测方法。首先，构建数据集，通过实地调研采样以及在实验室模拟等方法并利用图像处理技术构建完备的数据集；然后，利用深度学习语义分割技术对数据进行训练并对其优化；最后，采用图像处理技术对输出图像进行处理，以解决对积灰的定性定量分析。实验结果表明所提方法的有效性，可以应用于光伏电池板表面积灰的智能检测。

Research on ash deposition monitoring of photovoltaic modules based on semantic segmentation

In order to solve the problem that it is difficult to qualitatively and quantitatively analyze the gray of photovoltaic panel surface area under bad environment, an intelligent detection method of photovoltaic panel surface area gray based on deep learning algorithm is proposed. Firstly, the data set is constructed, and a complete data set is constructed by means of field investigation, sampling and Simulation in the laboratory, and image processing technology; Then, the deep learning semantic segmentation technology is used to train and optimize the data; Finally, the output image is processed by image processing technology to solve the qualitative and quantitative analysis of ash deposition. The experimental results show that the proposed method is effective and can be applied to the intelligent detection of photovoltaic panel surface ash.