Identification of dangerous driving state based on lightweight deep learning model. (January 2023)

• Record Type: Journal Article
• Authors: Song, Wei; Zhang, Guangde; Long, Yicheng
• Is Part Of: Computers & electrical engineering. Volume 105(2023)
• Publication Date: 2023-01
• Subjects: Dangerous driving -- Image classification -- Deep learning -- Lightweight networks; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108509
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Present a modified ShuffleNet lightweight classification model for four dangerous driving categories. Analyze the classification performance for different dangerous driving images. Explore the operation process and weight distribution of the modified ShuffleNet model for dangerous driving images. Abstract: Dangerous driving state is a significant cause of traffic accidents and traffic safety problems., and the relevant detection means are lacking at present. In order to monitor the status of drivers in real time, timely judge the dangerous driving time and give reminders, this paper proposes a hazardous driving image classification system based on a modified ShuffleNet lightweight model to deploy it flexibly inside a vehicle or in a particular detection device, which is trained, tested and applied on a professionally collected image dataset. The experimental results demonstrate that the proposed system can effectively reduce the model complexity, increase the speed of model operations and usage without significantly affecting the classification effect through comparing with traditional deep learning models. Various visualization schemes are used to analyze and compare the image feature information extracted by the various models to investigate the feature extraction effectiveness of lightweight deep learning image classification models. Graphical abstract: Graphical abstract Image, graphical abstract .