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Fast-PLDN: fast power line detection network

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Abstract

Obstacle detection, especially real-time power line detection plays a vital role in the low-altitude flight safety of aircrafts. Most of previous power line detection methods fail to deal with curved power lines due to the small size and unapparent visual features in the complex scene. In the paper, we propose a novel fast power line detection network (Fast-PLDN), a real-time semantic segmentation model, for pixel-wise straight and curved power line detection. Besides, we construct our network with low-high pass block and edge attention fusion module, which extract spatial and semantic information effectively to improve the power line detection result along the boundary. Furthermore, we also build up a new dataset named AIR Power Line dataset based on pixel-wise annotations for power line detection task because public Power Line dataset based on pixel-wise annotations is so limited. Our model can run at 189.6 frames per second (fps) with 71.3% mean intersection over union (mIoU) on AIR Power Line dataset, which outperforms most of the previous power line detection methods and the existing real-time semantic segmentation models.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China (2017YFF0107704).

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Authors and Affiliations

  1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China

    Kejian Zhu, Chenghua Xu & Gang Cai

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Kejian Zhu

  3. GeoDo (Beijing) Spatial Information Technology Co. Ltd., Beijing, 100190, China

    Chenghua Xu & Yucheng Wei

  4. Beijing Engineering Research Center of Digital City, Beijing, 100190, China

    Yucheng Wei

Authors
  1. Kejian Zhu
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  2. Chenghua Xu
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  3. Yucheng Wei
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  4. Gang Cai
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Correspondence to Chenghua Xu.

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Zhu, K., Xu, C., Wei, Y. et al. Fast-PLDN: fast power line detection network. J Real-Time Image Proc 19, 3–13 (2022). https://doi.org/10.1007/s11554-021-01154-3

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  • DOI: https://doi.org/10.1007/s11554-021-01154-3

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