@inproceedings{2af251075b8a444f81c685fedd5ef7d7,
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 = "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",
doi = "10.1109/ICAC55051.2022.9911167",
language = "English",
series = "2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Chenguang Yang and Yuchun Xu",
booktitle = "2022 27th International Conference on Automation and Computing",
}
