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

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


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.


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



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.


  1. 1.
    Avizonis, P., & Barron, B. (1999). Low cost wire detection system. In Proceedings of 18th Digital Avionics Systems Conference (Vol. 1, pp. 3–C). IEEE.Google Scholar
  2. 2.
    Bhola, R., Krishna, N. H., Ramesh, K. N., Senthilnath, J., & Anand, G. (2018). Detection of the power lines in UAV remote sensed images using spectral-spatial methods. Journal of Environmental Management, 206, 1233–1242.CrossRefGoogle Scholar
  3. 3.
    Candamo, J., Kasturi, R., Goldgof, D., & Sarkar, S. (2009). Detection of thin lines using low-quality video from low-altitude aircraft in urban settings. IEEE Transactions on Aerospace and Electronic Systems, 45(3), 937–949.CrossRefGoogle Scholar
  4. 4.
    Candamo, J., Goldgof, D., Kasturi, R., & Godavarthy, S. (2010). Detecting wires in cluttered urban scenes using a gaussian model. In 20th International Conference on Pattern Recognition (Vol. 1, pp. 432–435).Google Scholar
  5. 5.
    Cao, W., Zhu, L., Han, J., Wang, T., & Du, Y. (2013). High voltage transmission line detection for UAV based routing inspection. In IEEE/ASME International Conference on Advanced Intelligent Mechatronics (pp. 554–558).Google Scholar
  6. 6.
    Ceron, A., I. F. Mondragon, B., & Prieto, F. (2014). Power line detection using a circle based search with UAV images. In Proceedings of 2014 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 632–639).Google Scholar
  7. 7.
    Chen, Y., Li, Y., Zhang, H., Tong, L., Cao, Y., & Xue, Z. (2016). Automatic power line extraction from high resolution remote sensing imagery based on an improved radon transform. Pattern Recognition, 49, 174–186.CrossRefGoogle Scholar
  8. 8.
    Fernandes, L. A., & Oliveira, M. M. (2008). Real-time line detection through an improved Hough transform voting scheme. Pattern Recognition, 41(1), 299–314.CrossRefGoogle Scholar
  9. 9.
    Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems Man & Cybernetics, smc-3(6), 610–621.Google Scholar
  10. 10.
    Ji, J., Chen, G., & Sun, L. (2011). A novel Hough transform method for line detection by enhancing accumulator array. Pattern Recognition Letters, 32(11), 1503–1510.CrossRefGoogle Scholar
  11. 11.
    Karakose, E. (2017). Performance evaluation of electrical transmission line detection and tracking algorithms based on image processing using uav. In International artificial intelligence and data processing symposium (pp. 1–5).Google Scholar
  12. 12.
    Lenz, I., Lee, H., & Saxena, A. (2015). Deep learning for detecting robotic grasps. The International Journal of Robotics Research, 34(4–5), 705–724.Google Scholar
  13. 13.
    Li, Z., Liu, Y., Hayward, R., Zhang, J., & Cai, J. (2008). Knowledge-based power line detection for UAV surveillance and inspection systems. In Proceedings of 23rd International Conference on Image and Vision Computing, New Zealand (pp. 1–6). IEEE.Google Scholar
  14. 14.
    Li, Z., Liu, Y., Walker, R., Hayward, R., & Zhang, J. (2010). Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved hough transform. Machine Vision and Applications, 21(5), 677–686.CrossRefGoogle Scholar
  15. 15.
    Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88,Google Scholar
  16. 16.
    Liu, L., Wang, W., Yong, J., & Ren, J. (2016). Automatic extraction of power lines from aerial images based on Hough transform. In Proceedings of International Conference on Energy, Materials and Manufacturing Engineering, number Emme, page 78620Q.Google Scholar
  17. 17.
    Luo, X., Zhang, J., Cao, X., & Yan, P. (2014). Object-aware power line detection using color and near- infrared images. IEEE Transactions on Aerospace and Electronic Systems, 50(2), 1374–1389.CrossRefGoogle Scholar
  18. 18.
    Ma, Q., Goshi, D. S., Shih, Y. C., & Sun, M. T. (2011). An algorithm for power line detection and warning based on a millimeter-wave radar video. IEEE Transactions on Image Processing, 20(12), 3534–3543.MathSciNetCrossRefGoogle Scholar
  19. 19.
    Santos, T., Moreira, M., Almeida, J., et al. (2017). Plined: Vision-based power lines detection for unmanned aerial vehicles. In Proceedings of 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) (pp. 253–259). IEEE.Google Scholar
  20. 20.
    Sarabandi, K., & Park, M. (2000). Extraction of power line maps from millimeter-wave polarimetric SAR images. IEEE Transactions on Antennas and Propagation, 48(12), 1802–1809.CrossRefGoogle Scholar
  21. 21.
    Silver, D., Huang, A., Maddison, C. J., et al. (2016, January). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489.Google Scholar
  22. 22.
    Silver, D., Schrittwieser, J., Simonyan, K., et al. (2017, October). Mastering the game of go without human knowledge. Nature, 550, 354.Google Scholar
  23. 23.
    Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  24. 24.
    Song, B., & Li, X. (2014). Power line detection from optical images. Neurocomputing, 129, 350–361.CrossRefGoogle Scholar
  25. 25.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2818–2826).Google Scholar
  26. 26.
    Toft, P. (1996). The radon transform: Theory and implementation.Google Scholar
  27. 27.
    Wu, Q., An, J., & Yang, R. (2010). Extraction of power lines from aerial images based on Hough transform. In Earth observing missions and sensors: Development, implementation, and characterization (Vol. 7862, p. 78620Q). International Society for Optics and Photonics.Google Scholar
  28. 28.
    Yan, G. J., Li, C. Y., Zhou, G. Q., Zhang, W. M., & Li, X. W. (2007). Automatic extraction of power lines from aerial images. IEEE Geoscience and Remote Sensing Letters, 4(3), 387–391.CrossRefGoogle Scholar
  29. 29.
    Yetgin, O. E., & Gerek, O. N. (2013). Cable and wire detection system for aircrafts. In Proceedings of Signal Processing and Communications Applications Conference (pp. 1–4).Google Scholar
  30. 30.
    Yetgin, Ö. E., & Gerek, Ö. N. (2017a). Automatic recognition of scenes with power line wires in real life aerial images using DCT-based features. Digital Signal Processing: A Review Journal, 1, 1–18.Google Scholar
  31. 31.
    Yetgin, Ö. E., & Gerek, Ö. N. (2017b). Feature extraction, selection and classification code for power line scene recognition. SoftwareX.Google Scholar
  32. 32.
    Yetgin, Ö. E., & Gerek, Ö. N. (2017c). Powerline Image Dataset (Infrared-IR and Visible Light-VL). Mendeley Data, v7.Google Scholar
  33. 33.
    Yetgin, O. E., Senturk, Z., & Gerek, O. N. (2015). A comparison of line detection methods for power line avoidance in aircrafts. In Proceedings of 9th International Conference on Electrical and Electronics Engineering (ELECO) (pp. 241–245).Google Scholar
  34. 34.
    Zhang, J., Liu, L., Wang, B., et al. (2012). High speed automatic power line detection and tracking for a UAV-based inspection. In Proceedings of International Conference on Industrial Control and Electronics Engineering (pp. 266–269). IEEE.Google Scholar

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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