An embedded deep learning system for augmented reality in firefighting applications
This addresses the critical need for improved safety and navigation for firefighters in hazardous environments, though it is incremental as it applies existing deep learning models to a new application domain.
The researchers tackled the problem of situational awareness for firefighters by developing an embedded deep learning system that uses thermal, RGB, and depth imagery from cameras in personal protective equipment to analyze scenes in real time, with results visualized through augmented reality to highlight objects like doors and windows through smoke and flames.
Firefighting is a dynamic activity, in which numerous operations occur simultaneously. Maintaining situational awareness (i.e., knowledge of current conditions and activities at the scene) is critical to the accurate decision-making necessary for the safe and successful navigation of a fire environment by firefighters. Conversely, the disorientation caused by hazards such as smoke and extreme heat can lead to injury or even fatality. This research implements recent advancements in technology such as deep learning, point cloud and thermal imaging, and augmented reality platforms to improve a firefighter's situational awareness and scene navigation through improved interpretation of that scene. We have designed and built a prototype embedded system that can leverage data streamed from cameras built into a firefighter's personal protective equipment (PPE) to capture thermal, RGB color, and depth imagery and then deploy already developed deep learning models to analyze the input data in real time. The embedded system analyzes and returns the processed images via wireless streaming, where they can be viewed remotely and relayed back to the firefighter using an augmented reality platform that visualizes the results of the analyzed inputs and draws the firefighter's attention to objects of interest, such as doors and windows otherwise invisible through smoke and flames.