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Power Line Detection Based on Feature Fusion Deep Learning Network

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Advances in Computer Graphics (CGI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13443))

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Abstract

Nowadays, the network of transmission lines is gradually spreading all over the world. With the popularization of UAV and helicopter applications, it is of great significance for low-altitude safety aircraft to detect power lines in advance and implement obstacle avoidance. The Power Line Detection (PLD) in a complex background environment is particularly important. In order to solve the problem of false detection of power lines caused by complex background images, a PLD method based on feature fusion deep learning network is proposed in this paper. Firstly, in view of the problems of low accuracy and poor generalization by using the traditional PLD in complex background environments, a rough extraction module that makes full use of the fusion features is constructed, which is combined with the inherent features and auxiliary information of aerial power line images. Secondly, an output fusion module is constructed, the weights of which are actively learned in the network training session. Finally, the fusion module fuses the decisions of different depths for output. The experimental results show that the proposed method can effectively improve the accuracy of power line detection.

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, China

    Kuansheng Zou, Zhenbang Jiang & Shuaiqiang Zhao

Authors
  1. Kuansheng Zou
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  2. Zhenbang Jiang
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  3. Shuaiqiang Zhao
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Corresponding author

Correspondence to Kuansheng Zou .

Editor information

Editors and Affiliations

  1. University of Geneva, Geneva, Switzerland

    Nadia Magnenat-Thalmann

  2. Bournemouth University, Poole, UK

    Jian Zhang

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. University of Crete, Heraklion, Greece

    George Papagiannakis

  5. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  6. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

  7. University of Calgary, Calgary, AB, Canada

    Marina Gavrilova

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Zou, K., Jiang, Z., Zhao, S. (2022). Power Line Detection Based on Feature Fusion Deep Learning Network. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2022. Lecture Notes in Computer Science, vol 13443. Springer, Cham. https://doi.org/10.1007/978-3-031-23473-6_41

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  • DOI: https://doi.org/10.1007/978-3-031-23473-6_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23472-9

  • Online ISBN: 978-3-031-23473-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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