Deep learning assisted portable IR active imaging sensor spots and identifies live humans through fire

Bianco, Vittorio; Mazzeo, Pier Luigi; Paturzo, Melania; Distante, Cosimo; Ferraro, Pietro

doi:10.1016/j.optlaseng.2019.105818

Cited by 31 publications

(18 citation statements)

References 20 publications

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“…A pre-trained convolutional neural network is able to detect the presence of a person hidden behind fire in real time and accurately. Remarkably, the network can perform this task even when the system is not able to reject the flame contributions in full, thus improving its robustness [2]. Results demonstrate the possibility to generate alerts when persons are detected inside rooms invaded by flames.…”

Section: ! Introductionmentioning

confidence: 96%

“…Thus, the required laser characteristics are met only by bulky and heavy sources that cannot be made portable and are more suitable for fixed installations. Active non-interferometric IR imaging can discard the flame disturbance by operating as a selective narrow-bandpass receiver [2]. A low-coherence high power IR laser emits a beam that is expanded by a lens and directed toward the target.…”

Section: ! Introductionmentioning

confidence: 99%

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

Bianco,

Běhal,

Mazzeo

et al. 2023

Multimodal Sensing and Artificial Intelligence: Technologies and Applications III

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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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Section: ! Introductionmentioning

confidence: 96%

Section: ! Introductionmentioning

confidence: 99%

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

Bianco,

Běhal,

Mazzeo

et al. 2023

Multimodal Sensing and Artificial Intelligence: Technologies and Applications III

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show abstract

“…In recent years, deep learning has seen explosive growth in an enormous range of challenging computer vision applications [7][8][9][10][11][12]. In particular, deep learning has been playing an important role in enhancing the capabilities of various 3D imaging and shape reconstruction techniques via using supervised learning and massive datasets [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Accurate 3D Shape Reconstruction from Single Structured-Light Image via Fringe-to-Fringe Network

Nguyen

Wang

2021

Photonics

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Accurate three-dimensional (3D) shape reconstruction of objects from a single image is a challenging task, yet it is highly demanded by numerous applications. This paper presents a novel 3D shape reconstruction technique integrating a high-accuracy structured-light method with a deep neural network learning scheme. The proposed approach employs a convolutional neural network (CNN) to transform a color structured-light fringe image into multiple triple-frequency phase-shifted grayscale fringe images, from which the 3D shape can be accurately reconstructed. The robustness of the proposed technique is verified, and it can be a promising 3D imaging tool in future scientific and industrial applications.

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“…To propose a system that meets the two characteristics previously noted, this work presents a study of three firearm (handgun) detectors in images based on the application of convolutional neural networks (CNNs). While "classical" methods require the manual selection of discriminant features [3], CNNs are able to automatically extract complex patterns from data [4].…”

Section: Introductionmentioning

confidence: 99%

Automatic Handgun Detection with Deep Learning in Video Surveillance Images

et al. 2021

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There is a great need to implement preventive mechanisms against shootings and terrorist acts in public spaces with a large influx of people. While surveillance cameras have become common, the need for monitoring 24/7 and real-time response requires automatic detection methods. This paper presents a study based on three convolutional neural network (CNN) models applied to the automatic detection of handguns in video surveillance images. It aims to investigate the reduction of false positives by including pose information associated with the way the handguns are held in the images belonging to the training dataset. The results highlighted the best average precision (96.36%) and recall (97.23%) obtained by RetinaNet fine-tuned with the unfrozen ResNet-50 backbone and the best precision (96.23%) and F1 score values (93.36%) obtained by YOLOv3 when it was trained on the dataset including pose information. This last architecture was the only one that showed a consistent improvement—around 2%—when pose information was expressly considered during training.

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Deep learning assisted portable IR active imaging sensor spots and identifies live humans through fire

Cited by 31 publications

References 20 publications

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

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

Accurate 3D Shape Reconstruction from Single Structured-Light Image via Fringe-to-Fringe Network

Automatic Handgun Detection with Deep Learning in Video Surveillance Images

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