基于多无人机协同系统和YOLOv7的输电线路异物检测研究

IF 1.1 Q4 OPTICS Computer Optics Pub Date : 2023-10-01 DOI:10.18287/2412-6179-co-1257
R. Chang, Z.X. Mao, J. Hu, H.C. Bai, C.J. Zhou, Y. Yang, S. Gao
{"title":"基于多无人机协同系统和YOLOv7的输电线路异物检测研究","authors":"R. Chang, Z.X. Mao, J. Hu, H.C. Bai, C.J. Zhou, Y. Yang, S. Gao","doi":"10.18287/2412-6179-co-1257","DOIUrl":null,"url":null,"abstract":"The unique plateau geographical features and variable weather of Yunnan, China make transmission lines in this region more susceptible to coverage and damage by various foreign bodies compared to flat areas. The mountainous terrain also presents great challenges for inspecting and removing such objects. In order to improve the efficiency and detection accuracy of foreign body inspection of transmission lines, we propose a multi-UAV collaborative system specifically designed for the geographical characteristics of Yunnan's transmission lines in this paper. Additionally, the image data of foreign bodies was augmented, and the YOLOv7 target detection model, which offers a more balanced trade-off between precision and speed, was adopted to improve the accuracy and speed of foreign body detection.","PeriodicalId":46692,"journal":{"name":"Computer Optics","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on foreign body detection in transmission lines based on a multi-UAV cooperative system and YOLOv7\",\"authors\":\"R. Chang, Z.X. Mao, J. Hu, H.C. Bai, C.J. Zhou, Y. Yang, S. Gao\",\"doi\":\"10.18287/2412-6179-co-1257\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The unique plateau geographical features and variable weather of Yunnan, China make transmission lines in this region more susceptible to coverage and damage by various foreign bodies compared to flat areas. The mountainous terrain also presents great challenges for inspecting and removing such objects. In order to improve the efficiency and detection accuracy of foreign body inspection of transmission lines, we propose a multi-UAV collaborative system specifically designed for the geographical characteristics of Yunnan's transmission lines in this paper. Additionally, the image data of foreign bodies was augmented, and the YOLOv7 target detection model, which offers a more balanced trade-off between precision and speed, was adopted to improve the accuracy and speed of foreign body detection.\",\"PeriodicalId\":46692,\"journal\":{\"name\":\"Computer Optics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Optics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18287/2412-6179-co-1257\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Optics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18287/2412-6179-co-1257","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0

摘要

中国云南独特的高原地理特征和多变的天气使该地区的输电线路比平坦地区更容易受到各种异物的覆盖和破坏。山区地形也给检查和清除这些物体带来了巨大的挑战。为了提高输电线路异物检测的效率和检测精度,本文针对云南输电线路的地理特点,提出了一种专门设计的多无人机协同系统。此外,对异物图像数据进行增强,采用精度和速度更为平衡的YOLOv7目标检测模型,提高了异物检测的精度和速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Research on foreign body detection in transmission lines based on a multi-UAV cooperative system and YOLOv7
The unique plateau geographical features and variable weather of Yunnan, China make transmission lines in this region more susceptible to coverage and damage by various foreign bodies compared to flat areas. The mountainous terrain also presents great challenges for inspecting and removing such objects. In order to improve the efficiency and detection accuracy of foreign body inspection of transmission lines, we propose a multi-UAV collaborative system specifically designed for the geographical characteristics of Yunnan's transmission lines in this paper. Additionally, the image data of foreign bodies was augmented, and the YOLOv7 target detection model, which offers a more balanced trade-off between precision and speed, was adopted to improve the accuracy and speed of foreign body detection.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Computer Optics
Computer Optics OPTICS-
CiteScore
4.20
自引率
10.00%
发文量
73
审稿时长
9 weeks
期刊介绍: The journal is intended for researchers and specialists active in the following research areas: Diffractive Optics; Information Optical Technology; Nanophotonics and Optics of Nanostructures; Image Analysis & Understanding; Information Coding & Security; Earth Remote Sensing Technologies; Hyperspectral Data Analysis; Numerical Methods for Optics and Image Processing; Intelligent Video Analysis. The journal "Computer Optics" has been published since 1987. Published 6 issues per year.
期刊最新文献
Six-wave interaction with double wavefront reversal in multimode waveguides with Kerr and thermal nonlinearities Generation and study of the synthetic brain electron microscopy dataset for segmentation purpose Gradient method for designing cascaded DOEs and its application in the problem of classifying handwritten digits Method of multilayer object sectioning based on a light scattering model Investigation of polarization transformations performed with a refractive bi-conical axicon using the FDTD method
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1