Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (2): 327-337.DOI: 10.3778/j.issn.1673-9418.2009069

• Graphics and Image • Previous Articles     Next Articles

Algorithm for Real-Time Smoking Detection Based on Deep Learning

CHEN Ruilong, LUO Lei, CAI Zhiping, MA Wentao   

  1. College of Computer Science, National University of Defense Technology, Changsha 410073, China
  • Online:2021-02-01 Published:2021-02-01



  1. 国防科技大学 计算机学院,长沙 410073


In public, smoking behavior not only causes pathological harm to human health, but also exerts danger of fire hazards, etc. For the health and safety considerations, this paper designs a real-time smoking detection model based on deep learning for airports, gas stations, chemical warehouses and other places where smoking is strictly prohibited. This model uses a convolutional neural network to process the input frame from the video stream captured by the webcam. Through the process of image feature extraction, feature fusion, target classification and target posi-tioning, the coordinate of the cigarette is located, and then the smoking behaviors can be found out. Common object detection algorithms are not ideal for small target objects and the detection speed needs to be improved. This paper designs a series of convolutional neural network modules to reduce the amount of model parameters and pick up the inference speed to meet real-time requirements as well as improving the accuracy of small target object (cigarette) detection. This paper also comes up with some training skills to make the model more robust. Due to the lack of relevant dataset, this paper produces a dataset related to smoking behaviors. Through comparative experiments, it is proven that the algorithm proposed in this paper has better detection effects on proposed dataset and some public datasets.

Key words: computer vision, small object detection, real-time, smoking detection, robustness



关键词: 计算机视觉, 微型目标检测, 实时性, 吸烟检测, 鲁棒性