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Intelligent detection and processing capabilities can be instrumental toimproving the safety, efficiency, and successful completion of rescue missionsconducted by firefighters in emergency first response settings. The objectiveof this research is to create an automated system that is capable of real-time,intelligent object detection and recognition and facilitates the improvedsituational awareness of firefighters during an emergency response. We haveexplored state of the art machine/deep learning techniques to achieve thisobjective. The goal for this work is to enhance the situational awareness offirefighters by effectively exploiting the information gathered from infraredcameras carried by firefighters. To accomplish this, we use a trained deepConvolutional Neural Network (CNN) system to classify and identify objects ofinterest from thermal imagery in real time. In the midst of those criticalcircumstances created by structure fire, this system is able to accuratelyinform the decision making process of firefighters with real-time up-to-datescene information by extracting, processing, and analyzing crucial information.With the new information produced by the framework, firefighters are able tomake more informed inferences about the circumstances for their safe navigationthrough such hazardous and potentially catastrophic environments.

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