Enhancing public safety through advanced gateway application: integrating thermal sensing and audio classification for empowered emergency response

Abstract

Over the past few decades, the Internet of Things (IoT) and Artificial Intelligence (AI) have elevated a safer and more functioning system for public safety groups. Following such trends, this thesis introduces an optimized Android application with heat-sensing capabilities based on a thermal infrared camera and an AI model that identifies gunfire among other sounds. The solution incorporates an automatic dispatcher alert system and an IoT data reporting pipeline to external platforms, fostering an officer’s response to critical events such as gunfire and high heat sources in the field.

The thesis delves into the physical concepts of infrared thermography and the artificial neural network with transfer learning to provide an overview of key technologies implemented in the project. Moreover, the thesis documents the important processes throughout the project development phases, providing an overview of how the project was executed from start to end. The results and challenges are also evaluated for future development.

The project outcome is a well-functioning proof-of-concept application capable of thermal sensing, gunfire detection, and automatic initiation of alert systems for Android devices. Given successful testing and positive feedback from the team members, the solution demonstrates the advantages of interpolating IoT and AI into public safety services. Likewise, it offers future potential for productization under the Agnet ecosystem of Airbus Defence and Space.