IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i15p3910-d392588.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hongchen Li & Zhong Yang & Jiaming Han & Shangxiang Lai & Qiuyan Zhang & Chi Zhang & Qianhui Fang & Guoxiong Hu, 2020. "TL-Net: A Novel Network for Transmission Line Scenes Classification," Energies, MDPI, vol. 13(15), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3910-:d:392588
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/15/3910/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/15/3910/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ziquan Liu & Huifang Wang, 2018. "Automatic Detection of Transformer Components in Inspection Images Based on Improved Faster R-CNN," Energies, MDPI, vol. 11(12), pages 1-18, December.
    2. Zhenbing Zhao & Zhen Zhen & Lei Zhang & Yincheng Qi & Yinghui Kong & Ke Zhang, 2019. "Insulator Detection Method in Inspection Image Based on Improved Faster R-CNN," Energies, MDPI, vol. 12(7), pages 1-15, March.
    3. Jiaming Han & Zhong Yang & Hao Xu & Guoxiong Hu & Chi Zhang & Hongchen Li & Shangxiang Lai & Huarong Zeng, 2020. "Search Like an Eagle: A Cascaded Model for Insulator Missing Faults Detection in Aerial Images," Energies, MDPI, vol. 13(3), pages 1-20, February.
    4. Daqian Wei & Bo Wang & Gang Lin & Dichen Liu & Zhaoyang Dong & Hesen Liu & Yilu Liu, 2017. "Research on Unstructured Text Data Mining and Fault Classification Based on RNN-LSTM with Malfunction Inspection Report," Energies, MDPI, vol. 10(3), pages 1-22, March.
    5. Feifei Yang & David W. Wanik & Diego Cerrai & Md Abul Ehsan Bhuiyan & Emmanouil N. Anagnostou, 2020. "Quantifying Uncertainty in Machine Learning-Based Power Outage Prediction Model Training: A Tool for Sustainable Storm Restoration," Sustainability, MDPI, vol. 12(4), pages 1-19, February.
    6. Yao Liu & Jianmai Shi & Zhong Liu & Jincai Huang & Tianren Zhou, 2019. "Two-Layer Routing for High-Voltage Powerline Inspection by Cooperated Ground Vehicle and Drone," Energies, MDPI, vol. 12(7), pages 1-20, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chuanyang Liu & Yiquan Wu & Jingjing Liu & Jiaming Han, 2021. "MTI-YOLO: A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images," Energies, MDPI, vol. 14(5), pages 1-19, March.
    2. Jingjing Liu & Chuanyang Liu & Yiquan Wu & Huajie Xu & Zuo Sun, 2021. "An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images," Energies, MDPI, vol. 14(14), pages 1-19, July.
    3. Zahid Ali Siddiqui & Unsang Park, 2020. "A Drone Based Transmission Line Components Inspection System with Deep Learning Technique," Energies, MDPI, vol. 13(13), pages 1-24, June.
    4. Hughes, William & Zhang, Wei & Cerrai, Diego & Bagtzoglou, Amvrossios & Wanik, David & Anagnostou, Emmanouil, 2022. "A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    5. Kai Ding & Chen Yao & Yifan Li & Qinglong Hao & Yaqiong Lv & Zengrui Huang, 2022. "A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System," Sustainability, MDPI, vol. 14(10), pages 1-28, May.
    6. Yongcong Luo & Jing Ma & Chi Li, 2020. "Entity name recognition of cross-border e-commerce commodity titles based on TWs-LSTM," Electronic Commerce Research, Springer, vol. 20(2), pages 405-426, June.
    7. Leandro do C. Martins & Rafael D. Tordecilla & Juliana Castaneda & Angel A. Juan & Javier Faulin, 2021. "Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation," Energies, MDPI, vol. 14(16), pages 1-30, August.
    8. Jiaming Han & Zhong Yang & Hao Xu & Guoxiong Hu & Chi Zhang & Hongchen Li & Shangxiang Lai & Huarong Zeng, 2020. "Search Like an Eagle: A Cascaded Model for Insulator Missing Faults Detection in Aerial Images," Energies, MDPI, vol. 13(3), pages 1-20, February.
    9. Ju Sik Kim & Kyu Nam Choi & Sung Woo Kang, 2021. "Infrared Thermal Image-Based Sustainable Fault Detection for Electrical Facilities," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    10. Ahmed Daeli & Salman Mohagheghi, 2022. "Power Grid Infrastructural Resilience against Extreme Events," Energies, MDPI, vol. 16(1), pages 1-17, December.
    11. Tara C. Walsh & David W. Wanik & Emmanouil N. Anagnostou & Jonathan E. Mellor, 2020. "Estimated Time to Restoration of Hurricane Sandy in a Future Climate," Sustainability, MDPI, vol. 12(16), pages 1-27, August.
    12. Linfeng Wang & Heng Wan & Deqing Huang & Jiayao Liu & Xuliang Tang & Linfeng Gan, 2023. "Sustainable Analysis of Insulator Fault Detection Based on Fine-Grained Visual Optimization," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    13. Zhaoyun Zhang & Shihong Huang & Yanxin Li & Hui Li & Houtang Hao, 2022. "Image Detection of Insulator Defects Based on Morphological Processing and Deep Learning," Energies, MDPI, vol. 15(7), pages 1-17, March.
    14. Berk A. Alpay & David Wanik & Peter Watson & Diego Cerrai & Guannan Liang & Emmanouil Anagnostou, 2020. "Dynamic Modeling of Power Outages Caused by Thunderstorms," Forecasting, MDPI, vol. 2(2), pages 1-12, May.
    15. Jiyoung Woo & Jaeseok Yun, 2020. "Content Noise Detection Model Using Deep Learning in Web Forums," Sustainability, MDPI, vol. 12(12), pages 1-16, June.
    16. Miltiadis D. Lytras & Kwok Tai Chui, 2019. "The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications," Energies, MDPI, vol. 12(16), pages 1-7, August.
    17. James Campbell & Ángel Corberán & Isaac Plana & José M. Sanchis & Paula Segura, 2022. "Polyhedral analysis and a new algorithm for the length constrained K–drones rural postman problem," Computational Optimization and Applications, Springer, vol. 83(1), pages 67-109, September.
    18. Hughes, William & Zhang, Wei & Bagtzoglou, Amvrossios C. & Wanik, David & Pensado, Osvaldo & Yuan, Hao & Zhang, Jintao, 2021. "Damage modeling framework for resilience hardening strategy for overhead power distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    19. Feifei Yang & Diego Cerrai & Emmanouil N. Anagnostou, 2021. "The Effect of Lead-Time Weather Forecast Uncertainty on Outage Prediction Modeling," Forecasting, MDPI, vol. 3(3), pages 1-16, July.
    20. Yixing Wang & Meiqin Liu & Zhejing Bao & Senlin Zhang, 2018. "Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks," Energies, MDPI, vol. 11(5), pages 1-19, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3910-:d:392588. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.