Research on Photovoltaic Panel Image Processing Algorithm Based on Self-supervised Learning

This research centers on the image processing algorithm for photovoltaic panels based on self-supervised learning, aiming to enhance the accuracy and efficiency of defect detection and state evaluation of photovoltaic panels. During the research process, a large-scale photovoltaic panel image dataset was first constructed, covering normal and various defect states. Subsequently, the contrastive learning strategy was adopted as the core method of self-supervised learning, and a specialized network architecture was designed. Through different-perspective transformations of images, such as roll and cropping, the network learned the internal feature representation of the images. In the training stage, the principle of mutual information maximization was utilized to enable the model to capture the key information in the images and enhance the sensitivity to subtle defects. Experimental results show that this algorithm achieves an average accuracy of over 95% in the photovoltaic panel defect detection task. Compared with traditional supervised learning algorithms, its performance improves significantly in the case of small samples, effectively reducing the reliance on a large amount of labeled data. Meanwhile, in the state evaluation of photovoltaic panels in complex environments, it can accurately identify various abnormal conditions, providing strong support for the efficient operation and maintenance of photovoltaic power plants.