Distracted Driver Behavior Detection Based-on An Improved YOLOX Framework

Yajuan Wei; Zhaoli Guo; Chuan Dai; Minsi Chen; Zhijie Xu; Ying Liu; Jiulun Fan

doi:10.1109/ICAC55051.2022.9911167

Distracted Driver Behavior Detection Based-on An Improved YOLOX Framework

Yajuan Wei, Zhaoli Guo, Chuan Dai, Minsi Chen, Zhijie Xu, Ying Liu, Jiulun Fan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

With the surge of the number of cars, road traffic accidents occur frequently because of drivers' distracted attention and abnormal behaviors, which causes huge losses to people's lives and property. To alleviate this issue, an improved deep learning algorithm based on YOLOX framework was proposed in this research to detect driving behavior changes in live. An attention mechanism - Convolutional Block Attention Module (CBAM) - was introduced in multiple scales of feature layers to form the backbone of YOLOX network. A widely used data science competition platform was adopted for distracted behavior model training. The State Farm Distracted Driver Detection Dataset was used for model validation and performance benchmarking. Experimental results have indicated promising performance gain using the devised model over the original YOLOX framework in terms of mAP and inference time.

Original language	English
Title of host publication	2022 27th International Conference on Automation and Computing
Subtitle of host publication	Smart Systems and Manufacturing, ICAC 2022
Editors	Chenguang Yang, Yuchun Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	6
ISBN (Electronic)	9781665498074
ISBN (Print)	9781665498081
DOIs	https://doi.org/10.1109/ICAC55051.2022.9911167
Publication status	Published - 10 Oct 2022
Event	27th International Conference on Automation and Computing - Bristol, United Kingdom Duration: 1 Sep 2022 → 3 Sep 2022 Conference number: 27

Conference

Conference	27th International Conference on Automation and Computing
Abbreviated title	ICAC 2022
Country/Territory	United Kingdom
City	Bristol
Period	1/09/22 → 3/09/22

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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Wei, Y., Guo, Z., Dai, C., Chen, M., Xu, Z., Liu, Y., & Fan, J. (2022). Distracted Driver Behavior Detection Based-on An Improved YOLOX Framework. In C. Yang, & Y. Xu (Eds.), 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022 [9911167] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAC55051.2022.9911167

@inproceedings{d25259e3b8f840d3988378e6d57901a5,

title = "Distracted Driver Behavior Detection Based-on An Improved YOLOX Framework",

abstract = "With the surge of the number of cars, road traffic accidents occur frequently because of drivers' distracted attention and abnormal behaviors, which causes huge losses to people's lives and property. To alleviate this issue, an improved deep learning algorithm based on YOLOX framework was proposed in this research to detect driving behavior changes in live. An attention mechanism - Convolutional Block Attention Module (CBAM) - was introduced in multiple scales of feature layers to form the backbone of YOLOX network. A widely used data science competition platform was adopted for distracted behavior model training. The State Farm Distracted Driver Detection Dataset was used for model validation and performance benchmarking. Experimental results have indicated promising performance gain using the devised model over the original YOLOX framework in terms of mAP and inference time.",

keywords = "CBAM module, Distracted driving behavior, mAP, YOLOX",

author = "Yajuan Wei and Zhaoli Guo and Chuan Dai and Minsi Chen and Zhijie Xu and Ying Liu and Jiulun Fan",

note = "Funding Information: ACKNOWLEDGMENT This research is sponsored by Chinese National Natural Science Foundation (No. 62071378), and Natural Science Basic Research Plan in Shaanxi Province of China (No. 2022JM-353). Publisher Copyright: {\textcopyright} 2022 IEEE.; 27th International Conference on Automation and Computing, ICAC 2022 ; Conference date: 01-09-2022 Through 03-09-2022",

year = "2022",

month = oct,

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doi = "10.1109/ICAC55051.2022.9911167",

language = "English",

isbn = "9781665498081",

editor = "Chenguang Yang and Yuchun Xu",

booktitle = "2022 27th International Conference on Automation and Computing",

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address = "United States",

}

Wei, Y, Guo, Z, Dai, C, Chen, M, Xu, Z, Liu, Y & Fan, J 2022, Distracted Driver Behavior Detection Based-on An Improved YOLOX Framework. in C Yang & Y Xu (eds), 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022., 9911167, Institute of Electrical and Electronics Engineers Inc., 27th International Conference on Automation and Computing, Bristol, United Kingdom, 1/09/22. https://doi.org/10.1109/ICAC55051.2022.9911167

Distracted Driver Behavior Detection Based-on An Improved YOLOX Framework. / Wei, Yajuan; Guo, Zhaoli; Dai, Chuan et al.

2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022. ed. / Chenguang Yang; Yuchun Xu. Institute of Electrical and Electronics Engineers Inc., 2022. 9911167.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Distracted Driver Behavior Detection Based-on An Improved YOLOX Framework

AU - Wei, Yajuan

AU - Guo, Zhaoli

AU - Dai, Chuan

AU - Chen, Minsi

AU - Xu, Zhijie

AU - Liu, Ying

AU - Fan, Jiulun

N1 - Conference code: 27

PY - 2022/10/10

Y1 - 2022/10/10

N2 - With the surge of the number of cars, road traffic accidents occur frequently because of drivers' distracted attention and abnormal behaviors, which causes huge losses to people's lives and property. To alleviate this issue, an improved deep learning algorithm based on YOLOX framework was proposed in this research to detect driving behavior changes in live. An attention mechanism - Convolutional Block Attention Module (CBAM) - was introduced in multiple scales of feature layers to form the backbone of YOLOX network. A widely used data science competition platform was adopted for distracted behavior model training. The State Farm Distracted Driver Detection Dataset was used for model validation and performance benchmarking. Experimental results have indicated promising performance gain using the devised model over the original YOLOX framework in terms of mAP and inference time.

AB - With the surge of the number of cars, road traffic accidents occur frequently because of drivers' distracted attention and abnormal behaviors, which causes huge losses to people's lives and property. To alleviate this issue, an improved deep learning algorithm based on YOLOX framework was proposed in this research to detect driving behavior changes in live. An attention mechanism - Convolutional Block Attention Module (CBAM) - was introduced in multiple scales of feature layers to form the backbone of YOLOX network. A widely used data science competition platform was adopted for distracted behavior model training. The State Farm Distracted Driver Detection Dataset was used for model validation and performance benchmarking. Experimental results have indicated promising performance gain using the devised model over the original YOLOX framework in terms of mAP and inference time.

KW - CBAM module

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KW - mAP

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DO - 10.1109/ICAC55051.2022.9911167

M3 - Conference contribution

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SN - 9781665498081

BT - 2022 27th International Conference on Automation and Computing

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T2 - 27th International Conference on Automation and Computing

Y2 - 1 September 2022 through 3 September 2022

ER -

Distracted Driver Behavior Detection Based-on An Improved YOLOX Framework

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