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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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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

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  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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