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A Lightweight Modified YOLOv5 Network Using a Swin Transformer for Transmission-Line Foreign Object Detection

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成果类型:
期刊论文
作者:
Zhang, Dongsheng;Zhang, Zhigang;Zhao, Na;Wang, Zhihai
通讯作者:
Wang, ZH
作者机构:
[Zhang, Dongsheng] Yinchuan Univ Energy, Sch Elect Power, Yinchuan 750100, Peoples R China.
[Zhang, Zhigang] Changsha Univ Sci & Technol, Sch Phys & Elect Sci, Changsha 410114, Peoples R China.
[Zhao, Na] Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Changsha 410114, Peoples R China.
[Wang, Zhihai] Yinchuan Univ Energy, Sch Foreign Languages, Yinchuan 750100, Peoples R China.
通讯机构:
[Wang, ZH ] Y
Yinchuan Univ Energy, Sch Foreign Languages, Yinchuan 750100, Peoples R China.
语种:
英文
关键词:
YOLOv5;foreign object detection;lightweight network structure;Swin Transformer;attention mechanism
期刊:
Electronics
ISSN:
2079-9292
年:
2023
卷:
12
期:
18
页码:
3904-
基金类别:
Conceptualization, D.Z.; methodology, D.Z.; software, Z.Z.; validation, Z.Z.; formal analysis, D.Z.; investigation, Z.W.; data curation, N.Z.; writing—original draft preparation, D.Z.; writing—review and editing, D.Z.; visualization, Z.Z.; project administration, D.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by a project on undergraduate teaching quality from the Yinchuan University of Energy, grant number 2021-JG-X-02, the Ningxia Hui Autonomous Region college students’ innovation and entrepreneurship training program, grant number S202213820006, the Open Fund of the Key Laboratory of Highway Engineering of Ministry of Education, grant number kfj220201, and the Open Research Fund of Hunan Provincial Key Laboratory of Flexible Electronic Materials Genome Engineering, grant number 202019.
机构署名:
本校为其他机构
院系归属:
交通运输工程学院
物理与电子科学学院
摘要:
Transmission lines are often located in complex environments and are susceptible to the presence of foreign objects. Failure to promptly address these objects can result in accidents, including short circuits and fires. Existing foreign object detection networks face several challenges, such as high levels of memory consumption, slow detection speeds, and susceptibility to background interference. To address these issues, this paper proposes a lightweight detection network based on deep learning, namely YOLOv5 with an improved version of CSPDarknet and a Swin Transformer (YOLOv5-IC-ST). YOLOv5...

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