Installation method for visual identification and localization based on the structural characterization type of the solar frame type

Abstract With the rapid development of solar energy applications, efficient and accurate solar panel installation has become a key issue. A structural feature-based visual recognition and localization installation method is proposed in this paper based on solar frame type, aiming to improve the installation efficiency and accuracy. The method first utilizes deep learning algorithms for feature extraction and recognition of the solar frame structure to construct a high-precision frame structure model. Second, it combines visual recognition technology and sensor data to realize the accurate positioning and attitude adjustment of solar panels and frames. The experimental results show that the method can effectively recognize the frame structure in complex environments, and the positioning accuracy reaches the millimeter level, which significantly improves the installation efficiency and quality. This study provides a new technical path for the automated installation of solar panels, which has important engineering application value.