A dual-stage deep learning framework for simultaneous fire and firearm detection in smart surveillance systems

Ram Pravesh, Bikash Chandra Sahana,
A dual-stage deep learning framework for simultaneous fire and firearm detection in smart surveillance systems,
Results in Engineering,
Volume 27,
2025,
106330,
ISSN 2590-1230,
https://doi.org/10.1016/j.rineng.2025.106330.
(https://www.sciencedirect.com/science/article/pii/S2590123025024028)
Abstract: Traditional video surveillance systems often treat fire detection and firearm recognition as separate tasks, missing the opportunity to address multiple security threats in an integrated manner. This paper presents a novel dual-stage deep learning framework for real-time, unified detection of fire and firearms in smart surveillance environments. At its core, a Unified Threat Classification Network (UTCN) dynamically routes frames to lightweight YOLOv5n-based fire and firearm detectors, with a Multi-Frame Confidence Evaluator (MFCE) verifying detection consistency to reduce false positives and false negatives. Beyond extensive benchmark testing, the system was validated on unconstrained real CCTV footage and enhanced with a proof-of-concept domain adaptation loop that fine-tunes detection modules using few-shot real-world samples. The proposed framework achieved up to 97.1 % detection accuracy and demonstrated improved robustness in real deployment scenarios, confirming its suitability for scalable, edge-deployable smart surveillance systems.
Keywords: Fire detection; Firearm detection; Unified threat classification network; YOLOv5n; Multi-frame confidence evaluator; Real-time threat detection; Smart video surveillance; Integrated deep learning framework