Infrared Search and Track With Unbalanced Optimal Transport Dynamics Regularization

Bertrand, Nicholas P.; Lee, John; Prussing, Keith F.; Shapero, Samuel; Rozell, Christopher J.

doi:10.1109/lgrs.2020.3014534

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Rapid development of intelligent algorithms, digitization, networking and intelligent management, and control of power transmission inspection have become a trend of industrial development (Bertrand et al, 2021;Liu et al, 2019;Qiong & Wang, 2019). The traditional transmission line management can no longer meet the needs of developing technologies.…”

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Lu²,

Xu³

et al. 2023

International Journal of Information Technologies and Systems Approach

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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.

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Section: Introductionmentioning

confidence: 99%