{'fraction_non_alphanumeric': 0.02197802197802198, 'fraction_numerical': 0.007992007992007992, 'mean_word_length': 5.77027027027027, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Automatic Edge Segmentation of Surface Antenna for 3D Surface Detection

With the continuous development of radio application systems such as communication, broadcasting, radar, guidance and radio astronomy, people have an increasing demand for surface antennas, one of the important development trajectories. For the high-performance surface antenna, especially a large aperture surface antenna, its three-dimensional shape is helpful for advanced and scientific design. For the 3D reconstruction technology based on Pixel2Mesh, it is necessary to automatically segment the parabolic antenna. In this paper, deep learning technology is used to realize the semantic segmentation of surface antennas. The experimental results show that the designed SAUNet3+ network has excellent performance, and the Dice index can reach 81.39%. The automatic edge segmentation method of surface antenna provides a theoretical and experimental basis for the three-dimensional surface detection task of surface antenna.