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Object Detection for Rescue Operations by High-Altitude Infrared Thermal Imaging Collected by Unmanned Aerial Vehicles

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Pattern Recognition and Image Analysis (IbPRIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14062))

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Abstract

The analysis of the object detection deep learning model YOLOv5, which was trained on High-altitude Infrared Thermal (HIT) imaging, captured by Unmanned Aerial Vehicles (UAV) is presented. The performance of the several architectures of the YOLOv5 model, specifically ‘n’, ‘s’, ‘m’, ‘l’, and ‘x’, that were trained with the same hyperparameters and data is analyzed. The dependence of some characteristics, like average precision, inference time, and latency time, on different sizes of deep learning models, is investigated and compared for infrared HIT-UAV and standard COCO datasets. The results show that degradation of average precision with the model size is much lower for the HIT-UAV dataset than for the COCO dataset which can be explained that a significant amount of unnecessary information is removed from infrared thermal pictures (“pseudo segmentation”), facilitating better object detection. According to the findings, the significance and value of the research consist in comparing the performance of the various models on the datasets COCO and HIT-UAV, infrared photos are more effective at capturing the real-world characteristics needed to conduct better object detection.

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References

  1. Boccardo, P., Chiabrando, F., Dutto, F., Tonolo, F., Lingua, A.: UAV deployment exercise for mapping purposes: evaluation of emergency response applications. Sensors 15(7), 15717–15737 (2015)

    Article  Google Scholar 

  2. de Castro, A., Torres-Sánchez, J., Peña, J., Jiménez-Brenes, F., Csillik, O., López-Granados, F.: An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sens. 10(3), 285 (2018)

    Article  Google Scholar 

  3. Kanistras, K., Martins, G., Rutherford, M.J., Valavanis, K.P.: A survey of unmanned aerial vehicles (UAVs) for traffic monitoring. In: 2013 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 221–234 (2013)

    Google Scholar 

  4. Avola, D., Foresti, G.L., Martinel, N., Micheloni, C., Pannone, D., Piciarelli, C.: Aerial video surveillance system for small-scale UAV environment monitoring. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017)

    Google Scholar 

  5. Liu, Q., Shi, L., Sun, L., Li, J., Ding, M., Shu, F.S.: Path planning for UAV-mounted mobile edge computing with deep reinforcement learning. IEEE Trans. Veh. Technol. 69(5), 5723–5728 (2020)

    Article  Google Scholar 

  6. Wang, F., Zhang, M., Wang, X., Ma, X., Liu, J.: Deep learning for edge computing applications: a state-of-the-art survey. IEEE Access 8, 58322–58336 (2020)

    Article  Google Scholar 

  7. Suo, J., Wang, T., Zhang, X., Chen, H., Zhou, W., Shi, W.: HIT-UAV: a high-altitude infrared thermal dataset for unmanned aerial vehicles (2022)

    Google Scholar 

  8. Shamsoshoara, A.: The FLAME dataset: aerial Imagery Pile burn detection using drones (UAVs) (2020)

    Google Scholar 

  9. Liu, Q., He, Z., Li, X., Zheng, Y.: PTB-TIR: a thermal infrared pedestrian tracking benchmark. IEEE Trans. Multimedia 22(3), 666–675 (2020)

    Article  Google Scholar 

  10. Bondi, E., et al.: BIRDSAI: a dataset for detection and tracking in aerial thermal infrared videos. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1736–1745 (2020)

    Google Scholar 

  11. Beyerer, J., Ruf, M., Herrmann, C.: CNN-based thermal infrared person detection by domain adaptation. In: Dudzik, M.C., Ricklin, J.C. (eds.) Autonomous Systems: Sensors, Vehicles, Security, and the Internet of Everything,Orlando, USA, p. 8. SPIE (2018)

    Google Scholar 

  12. Levin, E., Zarnowski, A., McCarty, J.L., Bialas, J., Banaszek, A., Banaszek, S.: Feasibility study of inexpensive thermal sensors and small UAS deployment for living human detection in rescue missions application scenarios. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. XLI-B8, 99–103 (2016)

    Google Scholar 

  13. Gordienko, Y., et al.: Scaling analysis of specialized tensor processing architectures for deep learning models. Deep Learn. Concepts Archit. 65–99 (2020)

    Google Scholar 

  14. Gordienko, Y., et al.: “Last mile” optimization of edge computing ecosystem with deep learning models and specialized tensor processing architectures. In: Advances in computers, vol. 122, pp. 303–341. Elsevier (2021)

    Google Scholar 

  15. Taran, V., Gordienko, Y., Rokovyi, O., Alienin, O., Kochura, Y., Stirenko, S.: Edge intelligence for medical applications under field conditions. In: Hu, Z., Zhang, Q., Petoukhov, S., He, M. (eds.) ICAILE 2022. LNDECT, vol. 135, pp. 71–80. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-04809-8_6

    Chapter  Google Scholar 

  16. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  17. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361 (2012)

    Google Scholar 

  18. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010)

    Article  Google Scholar 

  19. Sudhakar, S., Vijayakumar, V., Kumar, C.S., Priya, V., Ravi, L., Subramaniyaswamy, V.: Unmanned aerial vehicle (UAV) based forest fire detection and monitoring for reducing false alarms in forest-fires. Comput. Commun. 149, 1–16 (2020)

    Article  Google Scholar 

  20. Bendea, H., Boccardo, P., Dequal, S., Tonolo, F.G., Marenchino, D., Piras, M.: Low cost UAV for post-disaster assessment. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 37, 1373-1379 (2008)

    Google Scholar 

  21. John Gunnar Carlsson and Siyuan Song: Coordinated logistics with a truck and a drone. Manage. Sci. 64(9), 4052–4069 (2018)

    Google Scholar 

  22. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25. Curran Associates Inc. (2012)

    Google Scholar 

  23. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015)

    Google Scholar 

  24. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv:1512.03385 [cs], p. 12 (2015)

  25. Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861 [cs], p. 9 (2017)

  26. Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448 (2015)

    Google Scholar 

  27. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. arXiv:1506.01497 [cs], p. 14 (2016)

  28. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, p. 10. IEEE (2016)

    Google Scholar 

  29. Liu, W., et al.: SSD: single shot multibox detector. arXiv:1512.02325 [cs], 9905:17 (2016)

  30. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  31. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: YOLOv4: optimal speed and accuracy of object detection (2020)

    Google Scholar 

  32. Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation (2018)

    Google Scholar 

  33. Li, S., Li, Y., Li, Y., Li, M., Xiaorong, X.: YOLO-FIRI: improved YOLOv5 for infrared image object detection. IEEE Access 9, 141861–141875 (2021)

    Article  Google Scholar 

  34. Kun, Z.: Background noise suppression in small targets infrared images and its method discussion. Opt. Optoelectron. Technol. 2, 9–12 (2004)

    Google Scholar 

  35. Anju, T.S., Nelwin Raj, N.R.: Shearlet transform based image denoising using histogram thresholding. In: 2016 International Conference on Communication Systems and Networks (ComNet), pp. 162–166 (2016)

    Google Scholar 

  36. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)

    Article  Google Scholar 

  37. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893 (2005)

    Google Scholar 

  38. Jocher, G., et al.: Ultralytics/YOLOv5: V7.0 - YOLOv5 SOTA realtime instance segmentation (2022)

    Google Scholar 

  39. Taran, V., et al.: Performance evaluation of deep learning networks for semantic segmentation of traffic stereo-pair images. In: Proceedings of the 19th International Conference on Computer Systems and Technologies, pp. 73–80 (2018)

    Google Scholar 

  40. Taran, V., Gordienko, Y., Rokovyi, A., Alienin, O., Stirenko, S.: Impact of ground truth annotation quality on performance of semantic image segmentation of traffic conditions. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds.) ICCSEEA 2019. AISC, vol. 938, pp. 183–193. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-16621-2_17

    Chapter  Google Scholar 

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Acknowledgements

This research was in part sponsored by the NATO Science for Peace and Security Programme under grant id. G6032.

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Authors and Affiliations

  1. National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine

    Andrii Polukhin, Yuri Gordienko & Sergii Stirenko

  2. Tallinn University of Technology, Tallinn, Estonia

    Gert Jervan

Authors
  1. Andrii Polukhin
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  2. Yuri Gordienko
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  3. Gert Jervan
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  4. Sergii Stirenko
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Correspondence to Andrii Polukhin .

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Editors and Affiliations

  1. University of Alicante, Alicante, Spain

    Antonio Pertusa

  2. University of Alicante, Alicante, Spain

    Antonio Javier Gallego

  3. Universitat Politècnica de València, Valencia, Spain

    Joan Andreu Sánchez

  4. IPO Porto, Coimbra, Portugal

    Inês Domingues

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Polukhin, A., Gordienko, Y., Jervan, G., Stirenko, S. (2023). Object Detection for Rescue Operations by High-Altitude Infrared Thermal Imaging Collected by Unmanned Aerial Vehicles. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_39

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  • DOI: https://doi.org/10.1007/978-3-031-36616-1_39

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