Title: TTower-345: a multi-categories multi-perspectives benchmark for automatic naming of transmission line inspection photos

Authors: Jinqing Shen; Hong Ye; Chunjun Tang; Guoqin Zhang; Yan He; Min Xie

Addresses: Jinhua Bada Group Ltd., Jinhua, Zhejiang, China ' Jinhua Bada Group Ltd., Jinhua, Zhejiang, China ' Jinhua Power Supply Company of State Grid Zhejiang Electric Power Co., Jinhua, Zhejiang, China ' China Jiliang Uinversity, Hangzhou, China ' Jinhua Power Supply Company of State Grid Zhejiang Electric Power Co., Jinhua, Zhejiang, China ' China Jiliang University, Hangzhou, China

Abstract: Efficient naming of inspection photos of transmission line towers is vital in the maintenance of power grid equipment. Current inspection photo naming methods are mainly manual, which is neither rapid nor effective. Research on inspection photo naming is limited due to a shortage of inspection image datasets and low image resolution. Hence, we gathered inspection photos of real tangent towers using drones and created an inspection photo dataset TTower-345 for automatic naming model training purposes. We proposed an automatic naming model, IELC (improved EfficientNet network and LBP classification model), based on this dataset. IELC comprises a dual-branch structure that integrates a jointly improved EfficientNet model and an local binary patterns (LBP) classification model. Experimental results indicated that the proposed dataset contains more diverse inspection image features, which in turn helped the model learn more features. In our experiments, our proposed automatic naming method achieved a classification accuracy of over 95% and demonstrated reliability by exhibiting good generalisability in practical scenarios.

Keywords: transmission lines; tangent towers; benchmark; inspection photo naming; EfficientNet model; local binary pattern; LBP classification.

DOI: 10.1504/IJAMECHS.2024.137554

International Journal of Advanced Mechatronic Systems, 2024 Vol.11 No.1, pp.11 - 25

Received: 13 Jul 2023
Accepted: 23 Oct 2023
Published online: 25 Mar 2024 *

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