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Deep learning system detects smoking in fire exits using YOLO models

By PulseAugur Editorial · [1 sources] ·

A research paper details a deep learning system designed for real-time smoking detection in fire exit zones using CCTV surveillance. The study evaluated YOLOv8, YOLOv11, and YOLOv12, developing a custom YOLOv8-derived model that achieved a recall of 78.90% and mAP of 83.70%. Performance tests on edge devices indicated the system is suitable for time-sensitive operations, with the Jetson Xavier NX processing data between 52 to 97 milliseconds per inference. AI

IMPACT Enhances public safety monitoring capabilities by providing automated detection of fire hazards in critical areas.

RANK_REASON This is a research paper detailing a novel application of deep learning for safety monitoring.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning system detects smoking in fire exits using YOLO models

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This is a research paper detailing a novel application of deep learning for safety monitoring.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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paper, safety
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High
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145 days old
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Sami Sadat, Mohammad Irtiza Hossain, Junaid Ahmed Sifat, Suhail Haque Rafi, Md. Waseq Alauddin Alvi, Md. Khalilur Rhaman ·

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

    arXiv:2508.11696v3 Announce Type: replace Abstract: A deep learning real-time smoking detection system for CCTV surveillance of fire exit areas is proposed due to critical safety requirements. The dataset contains 8,124 images from 20 different scenarios along with 2,708 raw samp…

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