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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2508.11696 |
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| _version_ | 1866917451679137792 |
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| author | Sadat, Sami Hossain, Mohammad Irtiza Sifat, Junaid Ahmed Rafi, Suhail Haque Alvi, Md. Waseq Alauddin Rhaman, Md. Khalilur |
| author_facet | Sadat, Sami Hossain, Mohammad Irtiza Sifat, Junaid Ahmed Rafi, Suhail Haque Alvi, Md. Waseq Alauddin Rhaman, Md. Khalilur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11696 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Deep Learning-Based CCTV System for Automatic Smoking Detection in Fire Exit Zones Sadat, Sami Hossain, Mohammad Irtiza Sifat, Junaid Ahmed Rafi, Suhail Haque Alvi, Md. Waseq Alauddin Rhaman, Md. Khalilur Computer Vision and Pattern Recognition Machine Learning 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. |
| title | A Deep Learning-Based CCTV System for Automatic Smoking Detection in Fire Exit Zones |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2508.11696 |