A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting
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 r...
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doaj-223e1986febb4eceb18a47fb805b327b2021-03-30T03:13:07ZengIEEEIEEE Access2169-35362020-01-018883088832110.1109/ACCESS.2020.29937679090877A Deep Learning Framework for Detection of Targets in Thermal Images to Improve FirefightingManish Bhattarai0https://orcid.org/0000-0002-1421-3643Manel Martinez-Ramon1https://orcid.org/0000-0001-6912-9951Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, USADepartment of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, USAIntelligent 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 up-to-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.https://ieeexplore.ieee.org/document/9090877/Deep convolutional neural networksinfrared imagesfirefighting environmentfirefighterssituational awareness |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Manish Bhattarai Manel Martinez-Ramon |
spellingShingle |
Manish Bhattarai Manel Martinez-Ramon A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting IEEE Access Deep convolutional neural networks infrared images firefighting environment firefighters situational awareness |
author_facet |
Manish Bhattarai Manel Martinez-Ramon |
author_sort |
Manish Bhattarai |
title |
A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting |
title_short |
A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting |
title_full |
A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting |
title_fullStr |
A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting |
title_full_unstemmed |
A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting |
title_sort |
deep learning framework for detection of targets in thermal images to improve firefighting |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
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 up-to-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. |
topic |
Deep convolutional neural networks infrared images firefighting environment firefighters situational awareness |
url |
https://ieeexplore.ieee.org/document/9090877/ |
work_keys_str_mv |
AT manishbhattarai adeeplearningframeworkfordetectionoftargetsinthermalimagestoimprovefirefighting AT manelmartinezramon adeeplearningframeworkfordetectionoftargetsinthermalimagestoimprovefirefighting AT manishbhattarai deeplearningframeworkfordetectionoftargetsinthermalimagestoimprovefirefighting AT manelmartinezramon deeplearningframeworkfordetectionoftargetsinthermalimagestoimprovefirefighting |
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