---

Journal of Vibration Testing and System Dynamics 6(4) (2022) 361--371 | DOI:10.5890/JVTSD.2022.12.002

Ke-Yuan Tang$^{1,2}$, Chuan-Li Liu$^{2}$, Le-Cai Cai$^{2}$, Kui Cheng$^2$, Xing Liu$^{1, 2}$, Shao-Song Duan$^{1, 2}$

$^{1}$ School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China

$^{2}$ Yibin University, Yibin 644000, China

Download Full Text PDF

 

Abstract

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$\mathrm{\times}$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.

Acknowledgments

This article is supposed by College Students' Innovative Entrepreneurial Training Plan Program, Sichuan University of Science \& Engineering (cx2020163).

References

1. [1]	Li, Q., Hsia, J., and Yang, G. (2011), Prevalence of smoking in China in 2010, New England Journal of Medicine, 364(25), 2469-2470.

2. [2]	\"{O}berg, M., Jaakkola, M.S., Woodward, A., Peruga, A., and Pr\"{u}ss-Ust\"{u}n, A. (2011), Worldwide burden of disease from exposure to second-hand smoke: a retrospective analysis of data from 192 countries, The Lancet, 377(9760), 139-146.

3. [3]	Sacks, J.J. and Nelson, D.E. (1994), Smoking and injuries: an overview, Preventive Medicine, 23(4), 515-520.

4. [4]	Stillman, F., Navas-Acien, A., Ma, J., Ma, S., Avila-Tang, E., Breysse, P., Yang, G., and Samet, J. (2007), Second-hand tobacco smoke in public places in urban and rural China, Tobacco Control, 16(4), 229-234.

5. [5]	Hesketh, T., Ding, Q.-J., and Tomkins, A. (2001), Smoking among youths in China, American Journal of Public Health, 91(10), 1653-1655.

6. [6]	Ballard, J.E., Koepsell, T.D., and Rivara, F. (1992), Association of smoking and alcohol drinking with residential fire injuries, American Journal of Epidemiology, 135(1), 26-34.

7. [7]	Bien, T.L. and Lin, C.H. (2013), Detection and recognition of indoor smoking events, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 8784, 878424.

8. [8]	Wu, W.-C. and Chen, C.-Y. (2011), Detection system of smoking behavior based on face analysis, 2011 Fifth International Conference on Genetic and Evolutionary Computing, 184-187.

9. [9]	Cui, J., Wang, L., Gu, T., Tao, X., and Lu, J. (2016), An audio-based hierarchical smoking behavior detection system based on a smart neckband platform, Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, New York, NY, USA, 190-199.

10. [10]	Shoaib, M., Scholten, H., Havinga, P.J.M., and Incel, O.D. (2016), A hierarchical lazy smoking detection algorithm using smartwatch sensors, 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), 1-6.

11. [11]	Zheng, X., Wang, J., Shangguan, L., Zhou, Z., and Liu, Y. (2016), Smokey: ubiquitous smoking detection with commercial WiFi infrastructures, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, 1-9.

12. [12]	Senyurek, V.Y., Imtiaz, M.H., Belsare, P., Tiffany, S., and Sazonov, E. (2019), Smoking detection based on regularity analysis of hand to mouth gestures, Biomedical Signal Processing and Control, 51, 106-112.

13. [13]	Yang, Z. and Yao, D. (2020), Fast TLAM: high-precision fine grain smoking behavior detection network, 2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP), 183-188.

14. [14]	Fu, L., Feng, Y., Wu, J., Liu, Z., Gao, F., Majeed, Y., Al-Mallahi, A., Zhang, Q., Li, R., and Cui, Y. (2020), Fast and accurate detection of kiwifruit in orchard using improved YOLOv3-tiny model, Precision Agric.

15. [15]	Han, B.-G., Lee, J.-G., Lim, K.-T., and Choi, D.-H. (2020), Design of a scalable and fast YOLO for edge-computing devices, Sensors, 20(23), 6779.

16. [16]	Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.-M. (2020), YOLOv4: optimal speed and accuracy of object detection, arXiv preprint arXiv:2004.10934.

17. [17]	Jiang, Z., Zhao, L., Li, S., and Jia, Y. (2020), Real-time object detection method based on improved YOLOv4-tiny, arXiv preprint arXiv:2011.04244.

18. [18]	Byeon, Y.H. and Kwak, K.C. (2017) A performance comparison of pedestrian detection using faster RCNN and ACF, 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 858-863.