Real Time Detection of Drivers’ Smoking Behavior Using the Improved YOLO-V4 Model

DOI

10.2991/978-94-6463-046-6_16How to use a DOI?

Keywords

Attention; Deep Learning; Real-Time Detection; Smoking Detecion; Yolo

Abstract

Drivers’ smoking behavior is one of the causes of traffic accidents. The traditional sensor-based smoking detection methods are expensive. Therefore, the method based on deep learning is used for smoking detection. However, due to the limitation of GPU computing hardware, deep learning detection algorithm is difficult to deploy. To solve this problem, this paper designs an improved YOLO-V4 lightweight target detection algorithm model, namely, SmokeNet, to detect smoking behavior, which not only has high recognition accuracy, but also can meet the conditions of real-time detection on the edge devices. First, we established a smoking data set, according to the characteristics of the dataset, we reconstructed the network structure of yolov4, reduced a large number of convolution layers, and retained only one head detection layer. Then, attention mechanism is introduced to improve the model fitting ability. Finally, we deploy the trained model in Jetson Xavier NX. Experiments show that the detection accuracy of smokenet is slightly lower than that of the original yolov4 model, but the size of the model is only 1/10 of that of yolov4, and the detection speed is increased by 57%.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Kaixin Zhao
PY  - 2022
DA  - 2022/12/17
TI  - Real Time Detection of Drivers’ Smoking Behavior Using the Improved YOLO-V4 Model
BT  - Proceedings of the 2022 2nd International Conference on Computer Technology and Media Convergence Design (CTMCD 2022)
PB  - Atlantis Press
SP  - 126
EP  - 134
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-046-6_16
DO  - 10.2991/978-94-6463-046-6_16
ID  - Zhao2022
ER  -