Multi-objective tracking for smart substation onsite surveillance based on YOLO Approach and AKCF-Reference-Cited by-同舟云学术

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Multi-objective tracking for smart substation onsite surveillance based on YOLO Approach and AKCF

Published:2023-10 Issue: Volume:9 Page:1429-1438
ISSN:2352-4847
Container-title:Energy Reports
language:en
Short-container-title:Energy Reports

Author:

Cai Dongsheng,Guan Zexiang,Bamisile Olusola,Zhang Wenxu,Li Jian,Zhang Zhenyuan,Wu Jie,Chang Zhengwei,Huang Qi

Funder

Sichuan Province Science and Technology Support Program

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

General Energy

Reference24 articles.

1. Cascaded feature enhancement network model for real-time video monitoring of power system;Long;Energy Rep,2021

2. Improved YOLOv4 algorithm for safety management of on-site power system work;Li;Energy Rep,2022

3. Anti-occlusion multi-object surveillance based on improved deep learning approach and multi-feature enhancement for unmanned smart grid safety;Cai;Energy Rep,2023

4. Study on the method of switch state detection based on image recognition in substation sequence control;Bin,2014

5. Rich feature hierarchies for accurate object detection and semantic segmentation;Girshick,2014

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on the detection algorithm of electric workers in the limited spaces of hydroelectric power station;Energy Reports;2024-12

2. Improved YoloV5 Model Target Detection Algorithm Based on Temporal Neural Networks;2023 International Conference on Mathematics, Intelligent Computing and Machine Learning;2023-12-15

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