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

• URL: http://arxiv.org/abs/2508.11696v2
• Date: Sat, 25 Oct 2025 17:26:43 GMT
• Title: A Deep Learning-Based CCTV System for Automatic Smoking Detection in Fire Exit Zones
• Authors: Sami Sadat, Mohammad Irtiza Hossain, Junaid Ahmed Sifat, Suhail Haque Rafi, Md. Waseq Alauddin Alvi, Md. Khalilur Rhaman,
• Abstract summary: A real-time smoking detection system for CCTV surveillance of fire exit areas is proposed due to critical safety requirements.<n>A dataset contains 8,124 images from 20 different scenarios along with 2,708 raw samples demonstrating low-light areas.<n>The proposed model outperformed the others, achieving a recall of 78.90 percent and mAP at 50 of 83.70 percent.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 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.

Related papers

• A Comparative Benchmark of Real-time Detectors for Blueberry Detection towards Precision Orchard Management [2.667064587590596]
  This study presents a novel comparative benchmark analysis of advanced real-time object detectors.<n>This dataset comprises 661 canopy images collected with smartphones during the 2022-2023 seasons.<n>Among the YOLO models, YOLOv12m achieved the best accuracy with a mAP@50 of 93.3%.
  arXiv  Detail & Related papers  (2025-09-24T21:42:24Z)
• YOLO-Vehicle-Pro: A Cloud-Edge Collaborative Framework for Object Detection in Autonomous Driving under Adverse Weather Conditions [8.820126303110545]
  This paper proposes two innovative deep learning models: YOLO-Vehicle and YOLO-Vehicle-Pro. YOLO-Vehicle is an object detection model tailored specifically for autonomous driving scenarios. YOLO-Vehicle-Pro builds upon this foundation by introducing an improved image dehazing algorithm.
  arXiv  Detail & Related papers  (2024-10-23T10:07:13Z)
• Optimizing YOLO Architectures for Optimal Road Damage Detection and Classification: A Comparative Study from YOLOv7 to YOLOv10 [0.0]
  This paper presents a comprehensive workflow for road damage detection using deep learning models. To accommodate hardware limitations, large images are cropped, and lightweight models are utilized. The proposed approach employs multiple model architectures, including a custom YOLOv7 model with Coordinate Attention layers and a Tiny YOLOv7 model.
  arXiv  Detail & Related papers  (2024-10-10T22:55:12Z)
• YOLORe-IDNet: An Efficient Multi-Camera System for Person-Tracking [2.5761958263376745]
  We propose a person-tracking system that combines correlation filters and Intersection Over Union (IOU) constraints for robust tracking. The proposed system quickly identifies and tracks suspect in real-time across multiple cameras. It is computationally efficient and achieves a high F1-Score of 79% and an IOU of 59% comparable to existing state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2023-09-23T14:11:13Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• COVID-19 Monitoring System using Social Distancing and Face Mask Detection on Surveillance video datasets [0.0]
  This paper proposes a comprehensive and effective solution to perform person detection, social distancing violation detection, face detection and face mask classification. The system performs with an accuracy of 91.2% and F1 score of 90.79% on the labelled video dataset and has an average prediction time of 7.12 seconds for 78 frames of a video.
  arXiv  Detail & Related papers  (2021-10-08T05:57:30Z)
• Oriented R-CNN for Object Detection [61.78746189807462]
  This work proposes an effective and simple oriented object detection framework, termed Oriented R-CNN. In the first stage, we propose an oriented Region Proposal Network (oriented RPN) that directly generates high-quality oriented proposals in a nearly cost-free manner. The second stage is oriented R-CNN head for refining oriented Regions of Interest (oriented RoIs) and recognizing them.
  arXiv  Detail & Related papers  (2021-08-12T12:47:43Z)
• Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge [57.647371468876116]
  We introduce our real-time 2D object detection system for the realistic autonomous driving scenario. Our detector is built on a newly designed YOLO model, called YOLOX. On the Argoverse-HD dataset, our system achieves 41.0 streaming AP, which surpassed second place by 7.8/6.1 on detection-only track/fully track, respectively.
  arXiv  Detail & Related papers  (2021-07-27T06:36:06Z)
• SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving [94.11868795445798]
  We release a Large-Scale Object Detection benchmark for Autonomous driving, named as SODA10M, containing 10 million unlabeled images and 20K images labeled with 6 representative object categories. To improve diversity, the images are collected every ten seconds per frame within 32 different cities under different weather conditions, periods and location scenes. We provide extensive experiments and deep analyses of existing supervised state-of-the-art detection models, popular self-supervised and semi-supervised approaches, and some insights about how to develop future models.
  arXiv  Detail & Related papers  (2021-06-21T13:55:57Z)
• 2nd Place Solution for Waymo Open Dataset Challenge - Real-time 2D Object Detection [26.086623067939605]
  In this report, we introduce a real-time method to detect the 2D objects from images. We leverage accelerationRT to optimize the inference time of our detection pipeline. Our framework achieves the latency of 45.8ms/frame on an Nvidia Tesla V100 GPU.
  arXiv  Detail & Related papers  (2021-06-16T11:32:03Z)
• Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection [99.94079901071163]
  This paper presents a novel end-to-end system for pedestrian detection using Dynamic Vision Sensors (DVSs) We target applications where multiple sensors transmit data to a local processing unit, which executes a detection algorithm. Our detector is able to perform a detection every 450 ms, with an overall testing F1 score of 83%.
  arXiv  Detail & Related papers  (2020-04-03T17:36:26Z)
• Inverting the Pose Forecasting Pipeline with SPF2: Sequential Pointcloud Forecasting for Sequential Pose Forecasting [106.3504366501894]
  Self-driving vehicles and robotic manipulation systems often forecast future object poses by first detecting and tracking objects. This detect-then-forecast pipeline is expensive to scale, as pose forecasting algorithms typically require labeled sequences of object poses. We propose to first forecast 3D sensor data and then detect/track objects on the predicted point cloud sequences to obtain future poses. This makes it less expensive to scale pose forecasting, as the sensor data forecasting task requires no labels.
  arXiv  Detail & Related papers  (2020-03-18T17:54:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.