RT-DETR for Intelligent Fire Detection: An Applied Framework for Smart Building and IoT Systems

Abstract

Fire and smoke detection in image data is a crucial application of computer vision; however, practical deployment in real-world environments remains challenging. While previous research has made significant progress; however, detection systems often struggle to maintain stability when faced with changing lighting conditions, partial occlusions, and the complex visual characteristics of indoor spaces. This study proposes an automated fire and smoke detection system based on the Real- Time Detection Transformer (RT-DETR) architecture. Unlike traditional models that focus solely on accuracy, this system is engineered to address the practical need for early warning, achieving a high recall of 91.6 % to minimise missed fire events. The system is designed for versatile integration into existing CCTV surveillance, Smart Building ecosystems, and IoT/Edge. Evaluated on the Home-Fire dataset using a five-fold cross-validation strategy, the model achieves an mAP@0.5 of 94.5 %. These results demonstrate that the proposed system offers a robust, scalable, and reliable solution for real-world fire safety monitoring, providing a robust foundation for autonomous fire safety monitoring and rapid emergency response.