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 proceedingConference contributionpeer-review

1 Citation (Scopus)


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 languageEnglish
Title of host publication2022 27th International Conference on Automation and Computing
Subtitle of host publicationSmart Systems and Manufacturing, ICAC 2022
EditorsChenguang Yang, Yuchun Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665498074
ISBN (Print)9781665498081
Publication statusPublished - 10 Oct 2022
Event27th International Conference on Automation and Computing - Bristol, United Kingdom
Duration: 1 Sep 20223 Sep 2022
Conference number: 27


Conference27th International Conference on Automation and Computing
Abbreviated titleICAC 2022
Country/TerritoryUnited Kingdom


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