Target Tracking Method for Transmission Line Moving Operation Based on Inspection Robot and Edge Computing

Author:

Li Ning1ORCID,Lu Jingcai1,Cheng Xu1,Tian Zhi1

Affiliation:

1. State Grid Hengshui Electric Power Supply Company, China

Abstract

Aiming at the problems of low accuracy and high loss rate when the traditional target tracking (TT) method is applied to the TT of the moving operation of the transmission line, a transmission line based on an inspection robot and edge computing (EC) is proposed: the mobile job TT method. First, the basic framework of the TT algorithm is proposed, relying on the edge device to develop the TT system for mobile operations on the transmission line. Video information is collected by an intelligent inspection robot and sent to the target tracking system in the edge device for processing to obtain accurate data. Then, the gradient disappearance and explosion problems caused by the increase of network depth are solved by using the deep residual network. The traditional deep residual network is improved by introducing the improved bidirectional feature reinforcement network and the classification and regression subnet. The loss of position texture information is remedied, and the accurate tracking of the moving target on transmission line is realized. Finally, the real-time data acquisition of mobile operation target is realized by using an intelligent inspection robot, and the experimental verification is conducted. The proposed algorithm is compared and analyzed against the three other algorithms using the same data set through simulation experiments. The results show the precision, recall rate, accuracy, and comprehensive evaluation index F1 value of the proposed algorithm rank highest, reaching 93.8%, 90.2%, 83.8%, and 89.8%, respectively, compared with the other algorithms.

Publisher

IGI Global

Subject

General Computer Science

Reference30 articles.

1. Infrared Search and Track With Unbalanced Optimal Transport Dynamics Regularization

2. Research on target detection based on distributed track fusion for intelligent vehicles.;B.Chen;Sensors (Basel),2020

3. Multiobjective Beamforming Power Control for Robust SINR Target Tracking and Power Efficiency in Multicell MU-MIMO Wireless System

4. Factors Impacting Defect Density in Software Development Projects

5. A fast moving target detection tracking and trajectory prediction system for binocular vision.;C.Guo;Wuhan University Journal of Natural Sciences,2021

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1. Machine Learning Algorithms for Intelligent Job Monitoring;2023 4th IEEE Global Conference for Advancement in Technology (GCAT);2023-10-06

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