Detecting Smoking Behaviors in Public Places Based on T-Yolov4-tiny - Tang, Ke-Yuan; Liu, Chuan-Li; Cai, Le-Cai; Cheng, Kui; Liu, Xing; Duan, Shao-Song (Journal of Vibration Testing and System Dynamics, 2026)
Smoking or passive smoking is not only harmful to health, but also easy to cause fires. However, there has been a lack of effective supervision of smoking behavior in public places. Most of the existing supervision methods rely on on-site patrols by supervisors, video surveillance or smoke alarms. These methods have problems such as low efficiency and low accuracy. In order to solve the problems, this paper proposes the T-Yolov4-tiny detection algorithm based on the Yolo lightweight network Yolov4-tiny. Specifically, multiple convolution layers are added to the original network to improve the CSPBlock Network structure; the 1×1 convolutional layers are used to reduce the amount of network calculations; the scale of the input image is increased to improve the ability of small target detection; the K-means clustering algorithm is utilized to optimize the anchor box size considering the actual target size in the data set, in order to improve the accuracy of the model. Then, a smoking behavior data set (named as Smoking-YBU) was collected by both web crawlers and manual collection. The experimental results show that, compared with the Yolov4-tiny algorithm, the mean average precision (mAP) of the T-Yolov4-tiny algorithm proposed in this paper increases by 12.69% on the Smoking-YBU, and the detection speed can also meet real-time requirements.