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

TL;DR

This paper presents a deep learning-based system utilizing CNNs to detect and recognize objects in thermal images, aiming to enhance firefighters' situational awareness and decision-making during emergency rescue operations.

Contribution

It introduces a real-time, automated thermal image analysis framework using deep learning to improve safety and efficiency in firefighting scenarios.

Findings

High accuracy in object detection from thermal images

Real-time processing capability demonstrated

Improved decision support for firefighters

Abstract

Intelligent detection and processing capabilities can be instrumental to 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 for this work is to enhance the situational awareness of firefighters by effectively exploiting the information gathered from infrared cameras carried by firefighters. 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 structure fire, this system is able to accurately inform the decision making process of firefighters with real-time up-to-date scene information by extracting, processing, and analyzing crucial information. With 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.