Deep learning-enabled far-infrared active imaging and video-surveillance through fire

Vittorio Bianco; Jaromír Běhal; Pier Luigi Mazzeo; Marika Valentino; Paolo Spagnolo; Lisa Miccio; Cosimo Distante; Pietro Ferraro

doi:10.1117/12.2674990

9 August 2023 Deep learning-enabled far-infrared active imaging and video-surveillance through fire

Vittorio Bianco, Jaromír Běhal, Pier Luigi Mazzeo, Marika Valentino, Paolo Spagnolo, Lisa Miccio, Cosimo Distante, Pietro Ferraro

Author Affiliations +

Proceedings Volume 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III; 126210W (2023) https://doi.org/10.1117/12.2674990
Event: SPIE Optical Metrology, 2023, Munich, Germany

Abstract

Achieving clear vision through smoke and flames is a highly pursued goal to better manage intervention priorities and to allow first responders operating safely during fire accidents. Here we show active far-infrared systems to image static/moving targets through fire with different imaging performance and field-portability characteristics. Low-coherence infrared systems and high-coherence holographic sensors will be discussed. We show that a pre-trained convolutional neural network can detect the presence of a person hidden behind fire in real-time, accurately, even when the system is not able to reject the flame contributions in full, being suitable for video-surveillance applications.

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Vittorio Bianco, Jaromír Běhal, Pier Luigi Mazzeo, Marika Valentino, Paolo Spagnolo, Lisa Miccio, Cosimo Distante, and Pietro Ferraro "Deep learning-enabled far-infrared active imaging and video-surveillance through fire", Proc. SPIE 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, 126210W (9 August 2023); https://doi.org/10.1117/12.2674990

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