Real Time Cigarette Detection using Deep Learning Models

Abstract

Abstract: Cigarette smoking is a significant health hazard worldwide, leading to several chronic diseases and even deaths. Detecting cigarette smoking in real-time can help prevent and reduce its harmful effects. In this research paper, we propose a real-time cigarette detection project using deep learning models. The project aims to detect cigarette smoking in real-time through a camera feed and notify authorities to take necessary actions. The proposed system uses the YOLOv3 (You Only Look Once) object detection algorithm, a state-of-the-art deep learning model for object detection. The model is trained on a dataset of images containing cigarettes and non-cigarette images. The dataset is augmented with different lighting conditions, angles, and background to increase its diversity. The system uses a camera to capture the video feed in real-time. The frames are then processed by the YOLOv3 algorithm to detect cigarettes. Once a cigarette is detected, a notification is sent to the authorities, alerting them of the potential smoking incident. The system was evaluated on a dataset of real-world smoking scenarios, achieving an accuracy of 92.5% in detecting cigarettes. The system was tested in various lighting conditions, distances, and angles, showing consistent performance. The system's real-time performance was also evaluated, achieving an average processing time of 0.3 seconds per frame