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A Detection Method for Typical Component of Space Aircraft Based on YOLOv3 Algorithm
He, Bian1,2,3; Jianzhong, Cao1,3; Cheng, Li1,3; Junpeng, Dong1,3; Zhongling, Ruan1,3; Chao, Mei1,3
2024
会议名称3rd IEEE International Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024
会议录名称2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024
页码1726-1729
会议日期2024-02-27
会议地点Changchun, China
出版者Institute of Electrical and Electronics Engineers Inc.
产权排序1
摘要

A solar panel recognition method based on YOLOv3 deep learning algorithm is proposed to address issues such as inaccurate recognition of traditional algorithms in space solar panel detection. First, this paper scales the dataset images to 416 × 416, then uses Labelme to annotate the data and transform the bounding box position information, and finally uses the YOLOv3 algorithm framework for model training. The results show that the recall, F1 score and accuracy of YOLOv3 algorithm are all above 80%. The YOLOv3 deep learning algorithm meets the requirements for real-time detection of solar panels in terms of accuracy. © 2024 IEEE.

关键词object detection Space debris Deep learning YOLOv3
作者部门飞行器光学成像与测量技术研究室
DOI10.1109/EEBDA60612.2024.10485846
收录类别EI
ISBN号9798350380989
语种英语
EI入藏号20241715982706
引用统计
文献类型会议论文
条目标识符http://ir.opt.ac.cn/handle/181661/97434
专题飞行器光学成像与测量技术研究室
通讯作者Zhongling, Ruan
作者单位1.Xi'an Institute of Optics and Precision Mechanics of Cas, Xi'an, China;
2.University of Chinese Academy of Sciences, Beijing, China;
3.Xi'an Key Laboratory of Spacecraft Optical Imaging and Measurement Technology, Xi'an, China
推荐引用方式
GB/T 7714
He, Bian,Jianzhong, Cao,Cheng, Li,et al. A Detection Method for Typical Component of Space Aircraft Based on YOLOv3 Algorithm[C]:Institute of Electrical and Electronics Engineers Inc.,2024:1726-1729.
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