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Minimal Kapur cross-entropy-based image segmentation for distribution grid inspection using improved INFO optimization algorithm

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成果类型:
期刊论文
作者:
Jiao, Junjun;Chen, Zhisheng;Zhou, Tao
通讯作者:
Chen, ZS
作者机构:
[Chen, Zhisheng; Zhou, Tao; Jiao, Junjun; Chen, ZS] Changsha Univ Sci Technol, Sch Elect & Informat Engn, Changsha 410114, Peoples R China.
通讯机构:
[Chen, ZS ] C
Changsha Univ Sci Technol, Sch Elect & Informat Engn, Changsha 410114, Peoples R China.
语种:
英文
关键词:
Distribution network;Unmanned aerial vehicle (UAV);Image segmentation;Improved weIghted meaN oF vectOrs (IINFO)
期刊:
JOURNAL OF SUPERCOMPUTING
ISSN:
0920-8542
年:
2024
卷:
80
期:
3
页码:
4309-4352
基金类别:
This work was supported in part by the Hunan Provincial Natural Science Foundation of China under Grant Nos. 2021JJ30732, the Young Teachers Program of Changsha University of Science & Technology under Grant No. 2019QJCZ041 and 2019QJCZ079. The authors gratefully appreciate this support.
机构署名:
本校为第一且通讯机构
院系归属:
电气与信息工程学院
摘要:
Distribution grid network has problems such as long mileage, large scale, complex surrounding environment, and aging of equipment. It is the development trend of power distribution network operation and maintenance to use unmanned aerial vehicles to patrol and combine with image processing technology for intelligent detection of equipment status. Image segmentation is well-known technique for extracting defect regions of equipment from distribution network inspection images. Therefore, this paper proposes an efficient a novel multilevel thresholding segmentation method to improve the fault dia...

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