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
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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。
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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.
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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.
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