SmartFire Vision: An Attention-Pruned Hybrid Vision Transformer and Detection Transformer Framework for Accurate, Efficient, and Real-Time Fire and Smoke Detection in Smart City Video Surveillance

Abstract

Fire incidents can lead to significant destruction of lives and property, especially in urban and smart cities, and pose a great risk worldwide. Existing fire and smoke detection systems are often inadequate for detecting the location of a fire, assessing the speed of its spread, and providing real-time alerts that can be acted upon quickly. This study proposes a method termed SmartFire Vision, which uses a hybrid deep learning framework consisting of an Efficient Vision Transformer (E-ViT) and a Detection Transformer (DETR) for real-time fire and smoke detection from video sequences. A major contribution of this study is the integration of a new Removing Inefficient Attention Heads (RIAH) pruning strategy to reduce the computational overhead and maintain a global context in the ViT encoder. The E-ViT and DETR feature representations were fused and passed to a fully connected classification head enhanced with a probabilistic thresholding function and an integrated alarm system. The proposed model was trained and evaluated using the FURG fire benchmark dataset, which comprises 28,022 annotated frames. The proposed model achieved an overall accuracy of 85.40%, precision of 85.33%, recall of 85.43%, and F1-score of 85.35%, surpassing the current state-of-the-art methods. The SmartFire Vision framework provides a highly capable and computationally efficient means of fire detection, is particularly beneficial for CCTV-based smart city surveillance, and shows promising computational efficiency on desktop-class GPUs, though dedicated edge-hardware validation remains a direction for future work.