Automatic generation of power distribution line ledgers using RFID and deep learning techniques

Wenbin Wang; Jianyong Liu; Shiyang Zhou

doi:10.1117/12.3034205

16 October 2024 Automatic generation of power distribution line ledgers using RFID and deep learning techniques

Wenbin Wang, Jianyong Liu, Shiyang Zhou

Author Affiliations +

Proceedings Volume 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024); 132911T (2024) https://doi.org/10.1117/12.3034205
Event: Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 2024, Changchun, China

Abstract

To facilitate the automatic generation of power distribution line ledgers, a technique harnessing both Radio Frequency Identification (RFID) and deep learning is introduced. Drones equipped with RFID tools are utilised to capture information from power distribution pylons, culminating in the creation of a geographical map of the power distribution line, thereby providing essential data support for automatic ledger generation. Subsequent to this, various models are employed to detect images of power distribution line equipment, with the Yolov5-s model being specifically employed for the identification of equipment on distribution line poles. Detection outcomes are subsequently displayed on the geographical map of the distribution line, culminating in the automatic generation of the power distribution line ledger. Results from this study indicate that the introduced method can effectively and swiftly generate power distribution line ledgers.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenbin Wang, Jianyong Liu, and Shiyang Zhou "Automatic generation of power distribution line ledgers using RFID and deep learning techniques", Proc. SPIE 13291, Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), 132911T (16 October 2024); https://doi.org/10.1117/12.3034205

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