用深度学习实时检测消防通道吸烟行为，提升公共安全监管效率。
A Deep Learning-Based CCTV System for Automatic Smoking Detection in Fire Exit Zones
- 基于YOLOv8改进，加入适应复杂监控场景的结构设计
- 召回率达78.90%，mAP50达83.70%，在多种环境下表现最优
- 可在Jetson Xavier NX上实现每帧52-97毫秒推理，适合实时部署
针对消防通道吸烟带来的安全隐患，提出一种基于深度学习的实时烟头检测系统。数据集包含来自20种不同场景的8,124张图像及2,708个低光环境下的原始样本。评估了YOLOv8、YOLOv11和YOLOv12三种先进目标检测模型，并在此基础上开发了一种基于YOLOv8的定制模型，增强了对复杂监控环境的适应性。所提模型性能最优，召回率达到78.90%，mAP@50为83.70%，在多变环境中实现了高效目标检测。在多种边缘设备上通过多线程运行测试，Jetson Xavier NX每帧推理耗时52至97毫秒，满足实时性要求。该系统为公共安全监控与自动合规监管提供了可靠平台。
原文摘要 · Abstract (English)
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 samples demonstrating low-light areas. We evaluated three advanced object detection models: YOLOv8, YOLOv11, and YOLOv12, followed by development of a custom model derived from YOLOv8 with added structures for challenging surveillance contexts. The proposed model outperformed the others, achieving a recall of 78.90 percent and mAP at 50 of 83.70 percent, delivering optimal object detection across varied environments. Performance evaluation on multiple edge devices using multithreaded operations showed the Jetson Xavier NX processed data at 52 to 97 milliseconds per inference, establishing its suitability for time-sensitive operations. This system offers a robust and adaptable platform for monitoring public safety and enabling automatic regulatory compliance.
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