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Automatic Power Line Detection for Low-Altitude Aircraft Safety Based on Deep Learning

Conference paper
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Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 549)

Abstract

Power line detection (PLD) is of vital importance for the flight security of low-altitude aircraft, such as helicopters and unmanned aerial vehicles (UAVs). This paper firstly summarises past studies on PLD based on image processing technique extensively. Secondly, different from the traditional PLD methods, we propose an approach to detect power lines based on deep learning which has been demonstrated having unparalleled performance in the field of image processing and computer vision. Specifically, the convolutional neural network (CNN) is employed in this study to extract features and thus detecting power lines from images. By utilising CNN, the feature extraction and object detection process are completed jointly unlike traditional PLD methods. A public dataset is used to demonstrate the performance of the proposed approach. This paper also gives recommendations for the future development of PLD.

Keywords

Power line detection Deep learning Image processing Low-Altitude flight safety 

Notes

Acknowledgements

This paper is sponsored by National Program on Key Basic Research Project (2014CB744903), National Natural Science Foundation of China (61673270), Shanghai Pujiang Program (16PJD028), Shanghai Industrial Strengthening Project (GYQJ-2017-5-08), Shanghai Science and Technology Committee Research Project (17DZ1204304) and Shanghai Engineering Research Center of Civil Aircraft Flight Testing.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Computer Science and Software EngineeringEast China Normal UniversityShanghaiChina

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