A Detection Method for Typical Component of Space Aircraft Based on YOLOv3 Algorithm | |
He, Bian1,2,3; Jianzhong, Cao1,3![]() | |
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 |
DOI | 10.1109/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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
A Detection Method f(1165KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
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