Object localization in handheld thermal images for fireground understanding

Vandecasteele, Florian; Merci, Bart; Jalalvand, Azarakhsh; Verstockt, Steven

doi:10.1117/12.2262484

Cited by 4 publications

(4 citation statements)

References 19 publications

(17 reference statements)

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“…Currently, fire services use thermal image cameras (TIC) in different types of emergencies related with fire and rescue [7]. Furthermore it can provide value real-time information during a enclosure fire.…”

Section: 4mentioning

confidence: 99%

“…The location and size of the vents in the enclosure maintains a close relation to the development of this kind of phenomenon. Vent identification using computer vision techniques [7] in real time could provide valuable information to predict flashover. Furthermore, flashover is clearly determined by an HRR/time relation, which could be a key issue for prediction with ANN models.…”

Section: Challenges An Opportunitiesmentioning

confidence: 99%

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A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires

et al. 2020

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Confined space fires are common emergencies in our society. Enclosure size, ventilation, or type and quantity of fuel involved are factors that determine the fire evolution in these situations. In some cases, favourable conditions may give rise to a flashover phenomenon. However, The difficulty of handling this complicated emergency through fire services can have fatal consequences for their staff. Therefore, there is a huge demand for new methods and technologies to tackle this life-threatening emergency. Modelling and simulation techniques have been adopted to conduct research due to the complexity of obtaining a real cases database related to this phenomenon. In this paper, a review of the literature related to the modelling and simulation of enclosure fires with respect to the flashover phenomenon is carried out. Furthermore, the related literature for comparing images from thermal cameras with computed images is reviewed. Finally, the suitability of artificial intelligence (AI) techniques for flashover prediction in enclosed spaces is also surveyed.

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Section: 4mentioning

confidence: 99%

Section: Challenges An Opportunitiesmentioning

confidence: 99%

A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires

et al. 2020

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“…In recent years, due to the incorporation of thermal imaging cameras (TIC) for fire services, has made it possible to detect several potentially dangerous situations for emergency services involved in fire and rescue. The cameras provide the user with valuable real-time information that, in the hands of trained professionals, can be of vital importance to their safety [3]. However, attention must be focused on certain types of fires in confined spaces, such as fires in which flashover phenomenon are likely to occur [1].…”

Section: 4mentioning

confidence: 99%

Fire Science Living Lab for Flashover Prediction

Cortés

Gil

Molina‐Azorín

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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There is a huge demand for new techniques and technologies to tackle life-threatening in fire emergencies. Enclosure fires are a type of emergency involving firefighters whose lives are sometimes put at risk. In any confined fire, the emergency team may encounter two types of combustion environments, ventilated or under-ventilated. The rapidly changing behaviour of this scenario depends on multiple factors such as enclosure size, ventilation, or type and quantity of fuel involved. However, the difficulty of handling this situation coupled with the potential for human error, even if there is undivided attention to the task in hand, remains an unresolved challenge for firefighters today. New technologies based in Thermal Imaging Cameras can help firefighters to prevent this situation. Fire Science Living Lab is presented as a solution to test new technologies for this area. This papers shows a resume about the process to develop a prediction system in a living lab for one of the most dangerous situation for firefighters, flashover.

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“…In [19], a CNN is used in material recognition with non-firefighter grade thermal cameras. A related study is reported in [20], where transfer learning is used to detect objects in a fireground. Nevertheless, the number of data samples used in the Vandecasteele paper is low, so deep learning cannot be applied.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting

Bhattarai

Martínez‐Ramón

2020

IEEE Access

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Intelligent detection and processing capabilities can be instrumental in improving the safety, efficiency, and successful completion of rescue missions conducted by firefighters in emergency first response settings. The objective of this research is to create an automated system that is capable of real-time, intelligent object detection and recognition and facilitates the improved situational awareness of firefighters during an emergency response. We have explored state-of-the-art machine/deep learning techniques to achieve this objective. The goal of this work is to enhance the situational awareness of firefighters by effectively exploiting the infrared video that is actively recorded by firefighters on the scene. To accomplish this, we use a trained deep Convolutional Neural Network (CNN) system to classify and identify objects of interest from thermal imagery in real-time. In the midst of those critical circumstances created by a structure fire, this system is able to accurately inform the decision-making process of firefighters with upto-date scene information by extracting, processing, and analyzing crucial information. Utilizing the new information produced by the framework, firefighters are able to make more informed inferences about the circumstances for their safe navigation through such hazardous and potentially catastrophic environments. INDEX TERMS Deep convolutional neural networks, infrared images, firefighting environment, firefighters, situational awareness.

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Object localization in handheld thermal images for fireground understanding

Cited by 4 publications

References 19 publications

A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires

A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires

Fire Science Living Lab for Flashover Prediction

A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting

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