@inproceedings{159c19a513bf4e36a7a3f0fe5dba6c55,
title = "Research on Surface Defect Detection Technology of Long-Distance and Long-Span FAST Cable",
abstract = "Aiming at the problems of large cable span, small number of defect samples and complex background environment in the FAST cable defect detection task, a set of real-time defect detection algorithms based on convolutional neural network is proposed to realize the accurate location and classification of defects. It can achieve a good detection effect for defects with multiple angles and sizes, especially suitable for medium and long distances. The algorithm is verified on the dataset, and its recognition accuracy can reach 91.7\%. Equipped on the hardware inference platform, it fully meets the efficiency and accuracy requirements of FAST cable inspection site, can be used in actual cable quality inspection tasks, and can be widely promoted to real-time defect detection of various high-altitude hanging cables.",
keywords = "FAST, cables, deep learning, defect detection",
author = "Xin Tong and Xuehe Zhang and Gangfeng Liu and Changle Li and Jie Zhao",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th International Conference on Mechatronics and Robotics Engineering, ICMRE 2023 ; Conference date: 10-02-2023 Through 12-02-2023",
year = "2023",
doi = "10.1109/ICMRE56789.2023.10106578",
language = "英语",
series = "2023 9th International Conference on Mechatronics and Robotics Engineering, ICMRE 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "225--228",
editor = "Yongsheng Ma",
booktitle = "2023 9th International Conference on Mechatronics and Robotics Engineering, ICMRE 2023",
address = "美国",
}