Deep learning system detects smoking in fire exits using YOLO models

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 →

• arXiv
• Jetson Xavier NX
• YOLOv11
• YOLOv12
• YOLOv8

• paper
• safety

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