Advertisement

The Role of Infrared Thermal Imaging in Road Patrolling Using Unmanned Aerial Vehicles

  • Neha Sharma
  • A. S. Arora
  • Ajay Pal Singh
  • Jaspreet Singh
Chapter

Abstract

In the past few years, the tremendous growth in road network and vehicles has increased the road fatalities at a very alarming rate. Road patrolling is one of the prominent measures to reduce road fatalities. Generally, road patrolling has been done using manned ground vehicles whose performance is highly dependent on environmental conditions. With this in mind, an infrared (IR) thermal imaging-based technique to enhance the object’s detection in poor weather conditions is presented in this study. Moreover, it can be employed in unmanned aerial vehicles (UAVs) for road patrolling in unfavorable weather conditions including total darkness, fog, and heavy rain. The aim of this study is to automate the process of object detection which enhances road patrolling, where it can enforce the traffic safety compliances and provide automatic rescue call facilities in case of remote area fatalities. The proposed approach is comprised of three steps: (a) data acquisition, a dataset of 53 thermograms at various weather conditions has been created; (b) data processing, a thresholding method, morphological operations, and pseudo-coloring have been performed; and (c) results validation, compare the outcomes of proposed methodology with standard approaches. More specifically, the optimal temperature thresholding in conjunction with morphological operations automates the process of object detection, where the pseudo-coloring algorithm is introduced to convert the thermograms into RGB space which enhances the images for better visualization. Consequently, the proposed methodology shows a good accuracy of 83% for object detection in different weather conditions. The methodology can be used with UAVs which enables fast monitoring of recent accidents on remote locations as the clashing of vehicles raises the temperature. Besides, the issues and challenges faced in the thermal-based UAVs are also discussed.

Keywords

Thermal imaging UAVs Night vision system Optimal temperature thresholding Pseudo-coloring 

References

  1. Arora S, Pandey R (2016) Applications of morphological operators using image morphological algorithms. SSRG Int J Electron Commun Eng 3:107–110Google Scholar
  2. Austin R (2011) Unmanned aircraft systems: UAVS design, development and deployment, vol 54. Wiley, SomersetGoogle Scholar
  3. Berni JA, Zarco-Tejada PJ, Suárez Barranco MD, Fereres Castiel E (2009) Thermal and narrow-band multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. Institute of Electrical and Electronics EngineersGoogle Scholar
  4. Bertozzi M, Broggi A, Fascioli A, Graf T, Meinecke MM (2004) Pedestrian detection for driver assistance using multi resolution infrared vision. IEEE Trans Veh Technol 53:1666–1678. conservation. Sensors 16: 97CrossRefGoogle Scholar
  5. Das A, Ghasemzadeh A, Ahmed MM (2019) Analyzing the effect of fog weather conditions on driver lane-keeping performance using the SHRP2 naturalistic driving study data. J Saf Res 68:71–80CrossRefGoogle Scholar
  6. Everaerts J (2008) The use of unmanned aerial vehicles (UAVs) for remote sensing and mapping. Int Arch Photogramm Remote Sens Spat Inf Sci 37:1187–1192Google Scholar
  7. Gade R, Moeslund TB (2014) Thermal cameras and applications: a survey. Mach Vis Appl 25(1):245–262CrossRefGoogle Scholar
  8. Gonzalez LF, Montes GA, Puig E, Johnson S, Mengersen K, Gaston KJ (2016) Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors 16:97CrossRefGoogle Scholar
  9. Goubet E, Katz J, Porikli F (2006) Pedestrian tracking using thermal infrared imaging. In: Infrared technology and applications XXXII 6206: 62062C. International Society for Optics and PhotonicsGoogle Scholar
  10. Jayadevan R, Navas KA (2014) Automated pseudo-coloring of grayscale images based on contour let transform. In: Communication: Signal Processing and Networking (NCCSN), IEEE, pp 1–6Google Scholar
  11. Luo Y, Remillard J, Hoetzer D (2010) Pedestrian detection in near-infrared night vision system. In: Intelligent vehicles symposium IV, 2010 IEEE, pp 51–58Google Scholar
  12. Nishar A, Richards S, Breen D, Robertson J, Breen B (2016) Thermal infrared imaging of geothermal environments and by an unmanned aerial vehicle (UAV): a case study of the Wairakei–Tauhara geothermal field, Taupo, New Zealand. Renew Energy 86:1256–1264CrossRefGoogle Scholar
  13. Omar M, Zhou Y (2007) Pedestrian tracking routine for passive automotive night vision systems. Sens Rev 27:310–316CrossRefGoogle Scholar
  14. Pandey RK, Mathurkar SS (2017) Implementation of parallel morphological filter with different structuring elements. In: 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), IEEE, pp 1450–1455Google Scholar
  15. Piniarski K, Pawłowski P, Dąbrowski A (2014) Pedestrian detection by video processing in automotive night vision system. In: Signal processing: algorithms, architectures, arrangements, and applications, IEEE, pp 104–109Google Scholar
  16. Quater PB, Grimaccia F, Leva S, Mussetta M, Aghaei M (2014) Light unmanned aerial vehicles (UAVs) for cooperative inspection of PV plants. IEEE J Photovolt 4:1107–1113CrossRefGoogle Scholar
  17. Rajkumar S, Mouli PC (2015) Pedestrian detection in infrared images using local thresholding. In: Electronics and communication Systems (ICECS), 2015 2nd international conference, IEEE pp 259–263Google Scholar
  18. Riggan PJ, Hoffman JW (2003) FireMapper™: a thermal-imaging radiometer for wildfire research and operations. In: Proceedings of the IEEE aerospace conferenceGoogle Scholar
  19. Saito H, Hagihara T, Hatanaka K, Sawat T (2008) Development of pedestrian detection system using far- infrared ray camera. SEI Tech Rev Engl Ed 66:112Google Scholar
  20. Singh SK (2017) Road traffic accidents in India: issues and challenges. Transportation Res Procedia 25:4708–4719CrossRefGoogle Scholar
  21. Singh J, Arora AS (2017) Contrast enhancement algorithm for IR Thermograms using optimal temperature Thresholding and contrast stretching. In Advances in machine learning and data science, Springer, pp 361–368Google Scholar
  22. Singh J, Arora AS (2018) An automated approach to enhance the thermographic evaluation on orofacial regions in lateral facial thermograms. J Therm Biol 71:91–98CrossRefGoogle Scholar
  23. Tsuji T, Hattori H, Watanabe M, Nagaoka N (2002) Development of night-vision system. IEEE Trans Intell Transp Syst 3:203–209CrossRefGoogle Scholar
  24. Zhang J, Jung J, Sohn G, Cohen M (2015) Thermal infrared inspection of roof insulation using unmanned aerial vehicles. Int Arch Photogramm Remote Sens Spat Inf Sci:40, 381–386CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Neha Sharma
    • 1
  • A. S. Arora
    • 2
  • Ajay Pal Singh
    • 1
  • Jaspreet Singh
    • 2
  1. 1.Department of Electronics and Communication EngineeringSant Longowal Institute of Engineering & TechnologyLongowal, SangrurIndia
  2. 2.Department of Electrical & Instrumentation EngineeringSant Longowal Institute of Engineering & TechnologyLongowal, SangrurIndia

Personalised recommendations