TL-Net: A Novel Network for Transmission Line Scenes Classification

Hongchen Li, Zhong Yang, Jiaming Han, Shangxiang Lai, Qiuyan Zhang, Chi Zhang, Qianhui Fang and Guoxiong Hu
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Hongchen Li: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 211100, China
Zhong Yang: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 211100, China
Jiaming Han: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 211100, China
Shangxiang Lai: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 211100, China
Qiuyan Zhang: Institute of Electric Power Science, Guizhou Power Grid Co., Ltd., 32 Jiefang Road, Guiyang 550002, China
Chi Zhang: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 211100, China
Qianhui Fang: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 211100, China
Guoxiong Hu: School of Software, Jiangxi Normal University, 437 Beijing West Road, Nanchang 330022, China

Energies, 2020, vol. 13, issue 15, 1-15

Abstract: With the development of unmanned aerial vehicle (UAV) control technology, one of the recent trends in this research domain is to utilize UAVs to perform non-contact transmission line inspection. The RGB camera mounted on UAVs collects large numbers of images during the transmission line inspection, but most of them contain no critical components of transmission lines. Hence, it is a momentous task to adopt image classification algorithms to distinguish key images from all aerial images. In this work, we propose a novel classification method to remove redundant data and retain informative images. A novel transmission line scene dataset, namely TLS_dataset, is built to evaluate the classification performance of networks. Then, we propose a novel convolutional neural network (CNN), namely TL-Net, to classify transmission line scenes. In comparison to other typical deep learning networks, TL-Nets gain better classification accuracy and less memory consumption. The experimental results show that TL-Net101 gains 99.68% test accuracy on the TLS_dataset.

Keywords: transmission lines inspection; unmanned aerial vehicle; image classification; deep neural network; voting classification strategy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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