Skip to main content
SpringerLink
Log in

Leveraging Spatial Context Disparity for Power Line Detection

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

For the safety of low flying aircraft, it will become increasingly important that an aircraft should have the ability to detect and avoid small obstacles in the low flying environment. In recent years, using context information to assist in detecting power lines has shown great potential to better detect power lines at a remote distance. Therefore, how to adequately use the context information for a better detection is a hot issue of concern. This paper proposes a novel auxiliary assisted power line detection method, in which the spatial context disparity of auxiliaries is quantitatively and uniformly evaluated for the first time. As a cognitive strategy, the spatial context disparity depends on two factors, the spatial context peakedness and the spatial context difference. With this cognitive method, objects that achieve high spatial context disparity scores are more suitable for being the auxiliaries of the power lines. Experimental results show that, owing to the spatial context disparity, the proposed method can acquire proper auxiliaries with abundant context information to support the detection, so that better power line detections are achieved comparing to traditional power line detection methods. The proposed power line detection method, which can automatically choose the optimal auxiliaries, is effective and has the potential for practical use in ensuring the flight safety of unmanned air vehicles (UAVs) in the low flying environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Gandhi T, Yang MT, Kasturi R, Camps O, Coraor L, McCandless J. Detection of obstacles in the flight path of an aircraft. IEEE Trans Aerosp Electron Syst. 2003;39(1):176–91.

    Article  Google Scholar 

  2. Yonemoto N, Yamamoto K, Yamada K, Yasui H, Tanaka N, Migliaccio C, et al. Performance of obstacle detection and collision warning system for civil helicopters. International Society for Optics and Photonics. 2006;622608:1–8.

    Google Scholar 

  3. Staff Writers. Enstrom to use safe slight power line unit. In: Reports of space mart, New York (UPI) http://www.spacemart.com/reports/Enstrom_to_use_Safe_Flight_power_line_unit_999.html, Oct 29, 2009.

  4. Candamo J, Kasturi R, Goldgof D, Sarkar S. Detection of thin lines using low-quality video from low-altitude aircraft in urban settings. IEEE Trans Aerosp Electron Syst. 2009;45(3):937–49.

    Article  Google Scholar 

  5. Song B, Li X. Power line detection from optical images. Neurocomputing. 2014;129:350–61.

    Article  Google Scholar 

  6. Clavelli A, Karatzas D, Lladós J, Ferraro M, Boccignone G. Modelling task-dependent eye guidance to objects in pictures. Cogn Comput. 2014;6(3):558–84.

    Article  Google Scholar 

  7. Li X, Guo Q, Lu X. Spatiotemporal statistics for video quality assessment. IEEE Trans Image Process. 2016;25(7):3329–42.

    Article  Google Scholar 

  8. Yubing T, Cheikh FA, Guraya FFE, Konik H, Trémeau A. A spatiotemporal saliency model for video surveillance. Cogn Comput. 2011;3(1):241–63.

    Article  Google Scholar 

  9. Pan J, Li X, Li X, Pang Y. Incrementally detecting moving objects in video with sparsity and connectivity. Cogn Comput. 2016;8(3):420–8.

    Article  Google Scholar 

  10. Gao F, Ma F, Zhang Y, Jun W, Sun J, Yang E, et al. Biologically inspired progressive enhancement target detection from heavy cluttered SAR images. Cognitive Computation. 2016.

  11. Jones D, Golightly I, Roberts J, Usher K. Modeling and control of a robotic power line inspection vehicle. In: Proceedings of the international conference on control applications, IEEE 2006. p. 632–637.

  12. Golightly I, Jones D. Visual control of an unmanned aerial vehicle for power line inspection. In: Proceedings of the 12th international conference on advanced robotics (ICAR’05), IEEE 2005. p. 288–295.

  13. Jones D, Earp GK. Camera sightline pointing requirements for aerial inspection of overhead power lines. Electr Power Syst Res. 2001;57(2):73–82.

    Article  Google Scholar 

  14. Whitworth CC, Duller AWG, Jones D, Earp GK. Aerial video inspection of overhead power lines. Power Engineering Journal. 2001;15(1):25–32.

    Article  Google Scholar 

  15. Candamo J, Goldgof D, Kasturi R, Godavarthy S. Detecting wires in cluttered urban scenes using a Gaussian model. In: Proceedings of the 20th international conference on pattern recognition(ICPR), IEEE 2010. p. 432–435.

  16. Candamo J, Goldgof D. Wire detection in low-altitude, urban, and low-quality video frames. In: Proceedings of the 19th international conference on pattern recognition(ICPR), IEEE 2008. p. 1–4.

  17. Candamo J, Kasturi R, Goldgof D, Sarkar S. Vision-based on-board collision avoidance system for aircraft navigation. International Society for Optics and Photonics. 2006;62300X:1–7.

    Google Scholar 

  18. Gaspar ZRS, Du S, vanWyk BJ. Hough transform tuned bayesian classifier for overhead power line inspection. In: Proceedings of the 19th annual symposium of the pattern recognition association of south Africa, 2008. p. 137–140.

  19. Cerón A, Iván F, Mondragón B, Prieto F. Towards visual based navigation with power dine detection. In: Advances in Visual Computing, Springer International Publishing 2014. 8887:827–36.

  20. Zhang J, Liu L, Wang B, Chen X, Wang Q, Zheng T. High speed automatic power line detection and tracking for a UAV-based inspection. In: Proceedings of international conference on industrial control and electronics engineering (ICICEE), IEEE 2012. p. 266–269.

  21. Cao W, Zhu L, Han J, Wang T, Du Y. High voltage transmission line detection for uav based routing inspection. In: Proceedings of international conference on advanced intelligent mechatronics (AIM), 2013 IEEE/ASME, IEEE 2013, p. 554–558.

  22. Luo X, Zhang J, Cao X, Yan P, Li X. Object-aware power line detection using color and near-infrared images. IEEE Trans Aerosp Electron Syst. 2014;50(2):1374–89.

    Article  Google Scholar 

  23. Kasturi R, Camps OI, Huang Y, Narasimhamurthy A, Pande N. Wire detection algorithms for navigation. In: 2002. Technical Reports, NASA.

  24. Yan G, Li C, Zhou G, Zhang W, Li X. Automatic extraction of power lines from aerial images. IEEE Geosci Remote Sens Lett. 2007;4(3):387–91.

    Article  Google Scholar 

  25. Cerón A, Mondragón BIF, Prieto F. Power line detection using a circle based search with UAV images. In: Proceedings of international conference on unmanned aircraft systems (ICUAS) 2014. p. 632–639.

  26. Zhou G, Yuan J, Yen I-L, Bastani F. Robust real-time UAV based power line detection and tracking. In: Proceedings of international conference on image processing (ICIP). IEEE 2016. p. 744–748.

  27. Golightly I, Jones D. Corner detection and matching for visual tracking during power line inspection. Image Vis Comput. 2003;21(9):827–40.

    Article  Google Scholar 

  28. Li Z, Liu Y, Walker R, Hayward R, Zhang J. Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform. Mach Vis Appl. 2010;21(5): 677–86.

    Article  Google Scholar 

  29. Li Z, Liu Y, Hayward R, Zhang J, Cai J. Knowledge-based power line detection for UAV surveillance and inspection systems. In: Proceedings of 23th international conference on image and vision computing New Zealand 2008. IEEE 2008. p. 1–6.

  30. Zhang J, Shan H, Cao X, Yan P, Li X. Pylon line spatial correlation assisted transmission line detection. IEEE Trans Aerosp Electron Syst. 2014;50(4):2890–905.

    Article  Google Scholar 

  31. Shan H, Zhang J, Cao X. Power line detection using spatial contexts for low altitude environmental awareness. In: Proceedings of international conference on integrated communication, navigation, and surveillance (ICNS). IEEE 2015. W2:1–10.

  32. Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell. 2002;24(4):509–22.

    Article  Google Scholar 

  33. Wang X, Bai X, Liu W, Latecki LJ. Feature context for image classification and object detection. In: Proceedings of conference on computer vision and pattern recognition (CVPR). IEEE 2011. p. 961–968.

  34. Cao W, Yang X, Zhu L, Han J, Wang T. Power line detection based on symmetric partial derivative distribution prior. In: Proceedings of international conference on information and automation (ICIA), IEEE 2013. p. 767–772.

  35. Cheng W, Song Z. Power pole detection based on graph cut. In: Congress on image and signal processing (CISP), IEEE 2008. p. 720–724.

  36. Bertasius G, Shi J, Torresani L. DeepEdge: A multi-scale bifurcated deep network for top-down contour detection. In: Proceedings of conference on computer vision and pattern recognition (CVPR). IEEE 2015. p. 4380–4389.

  37. Shen W, Wang X, Wang Y, Bai X, Zhang Z. DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection. In: Proceedings of conference on computer vision and pattern recognition (CVPR). IEEE 2015. p. 3982–3991.

  38. Xie S, Tu Z. Holistically-Nested Edge Detection. In: Proceedings of international conference on computer vision (ICCV). IEEE 2015. p. 1395–1403.

  39. Lu X, Yuan Y, Zheng X. Joint dictionary learning for multispectral change detection. IEEE Transactions on Cybernetics. 2016;PP(99):1–14.

    Google Scholar 

  40. Zheng WS, Gong S, Xiang T. Quantifying and transferring contextual information in object detection. IEEE Trans Pattern Anal Mach Intell. 2012;34(4):762–77.

    Article  PubMed  Google Scholar 

  41. Zheng WS, Gong S, Xiang T. Quantifying contextual information for object detection. In: Proceedings of 12th international conference on computer vision(ICCV), IEEE 2009. p. 932–939.

  42. Gioi GR, Jakubowicz J, Morel JM, Randall G. LSD: a fast line segment detector with a false detection control. IEEE Trans Pattern Anal Mach Intell. 2010;32(4):722.

    Article  Google Scholar 

  43. Lowe DG. Distinctive image features from scale invariant keypoints. Int J Comput Vis. 2004;60(2):91–110.

    Article  Google Scholar 

  44. Peng X, Yu Z, Yi Z, Tang H. Constructing the L2-graph for robust subspace learning and subspace clustering. IEEE Transactions on Cybernetics. 2017;47(4):1053–66.

    Article  PubMed  Google Scholar 

  45. Peng X, Tang H, Zhang L, Yi Z, Xiao S. A unified framework for representation-based subspace clustering of out-of-sample and large-scale data. IEEE Transactions on Neural Networks and Learning Systems. 2016; 27(12):2499–512.

    Article  PubMed  Google Scholar 

  46. Lu X, Li X, Mou L. Semi-supervised multitask learning for scene recognition. IEEE Transactions on Cybernetics. 2015;45(9):1967–76.

    Article  PubMed  Google Scholar 

  47. Lu X, Zheng X, Li X. Latent semantic minimal hashing for image retrieval[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society. 2016;26(1):355–68.

    Article  Google Scholar 

  48. Hartigan JA, Wong MA. Algorithm AS 136: A k-means clustering algorithm. J R Stat Soc. 1979;28(1):100–8.

    Google Scholar 

  49. Scardapane S, Uncini A. Semi-supervised echo state networks for audio classification. Cogn Comput. 2017; 9(1):125–35.

    Article  Google Scholar 

  50. Thanh ND, Ali M, Son LH. A novel clustering algorithm in a neutrosophic recommender system for medical diagnosis. Cognitive Computation 2017. p. 1–19.

  51. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–97.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Beihang University, and National Key Laboratory of CNS/ATM, Beijing, 100191, People’s Republic of China

    Chaofeng Pan, Haotian Shan, Xianbin Cao & Dapeng Wu

  2. The Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, 710119, Shaanxi, People’s Republic of China

    Xuelong Li

  3. The University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China

    Xuelong Li

  4. School of Electronics and Information Engineering, Beihang University, Beijing Laboratory for General Aviation Technology, Beijing, 100191, People’s Republic of China

    Chaofeng Pan & Haotian Shan

  5. University of Florida, Gainesville, FL, USA

    Dapeng Wu

Authors
  1. Chaofeng Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haotian Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xianbin Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuelong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dapeng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xianbin Cao.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Informed Consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Informed consent was obtained from all patients for being included in the study. Additional informed consent was obtained from all patients for which identifying information is included in this article.

Human and Animal Rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Funding

This study was funded by the National key research and 746 development program (Grant No. 2016YFB1200100), the National 747 Science Fund for Distinguished Young Scholars (Grant No. 61425014) 748, the Foundation for Innovative Research Groups of the National 749 Natural Science Foundation of China (Grant No. 61521091), the National Natural Science Foundation of China (Grant No. 61761130079), and by Key Research Program of Frontier Sciences, CAS (Grant No. QYZDY-SSW-JSC044).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pan, C., Shan, H., Cao, X. et al. Leveraging Spatial Context Disparity for Power Line Detection. Cogn Comput 9, 766–779 (2017). https://doi.org/10.1007/s12559-017-9488-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-017-9488-y

Keywords

Search

Navigation