A Deep Learning-Based CCTV System for Automatic Smoking Detection in Fire Exit Zones

A deep learning real-time smoking detection system for CCTV surveillance of fire exit areas is proposed in this research due to its critical safety requirements. The dataset contained 8,124 images which came from 20 different scenarios along with images from 2,708 raw samples demonstrating low-light areas. We implemented an evaluation of three advanced object detection models which included YOLOv8 and YOLOv11 and YOLOv12 followed by development of our custom model that derived its design from YOLOv8 through added structures for facing demanding surveillance contexts. The proposed model outperformed other evaluated models by reaching recall of 78.90% and mAP@50 of 83.70% to deliver optimal object identification and detection results across different environments. A performance evaluation for inference involved analysing multiple edge devices through mul-tithreaded operations. The Jetson Xavier NX processed information at the fastest real-time rate of 52-97 ms which established its suitability for time-sensitive operations. The study establishes the proposed system delivers a fair and adjustable platform to monitor public safety processes while enabling automatic regulatory compliance checks.