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Performance comparison of deep cnn models for detecting driver's distraction
, L. Garg, D. Datta, A.A. Alaboudi, N.Z. Jhanjhi, R. Agarwal, A.G. Thomas
Published in Tech Science Press
2021
Volume: 68
   
Issue: 3
Pages: 4109 - 4124
Abstract
According to various worldwide statistics, most car accidents occur solely due to human error. The person driving a car needs to be alert, especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident. Even though semiautomated checks, such as speed detecting cameras and speed barriers, are deployed, controlling human errors is an arduous task. The key causes of driver's distraction include drunken driving, conversing with co-passengers, fatigue, and operating gadgets while driving. If these distractions are accurately predicted, the drivers can be alerted through an alarm system. Further, this research develops a deep convolutional neural network (deep CNN) models for predicting the reason behind the driver's distraction. The deep CNN models are trained using numerous images of distracted drivers. The performance of deepCNNmodels, namely theVGG16,ResNet, and Xception network, is assessed based on the evaluation metrics, such as the precision score, the recall/sensitivity score, the F1 score, and the specificity score. The ResNet model outperformed all other models as the best detection model for predicting and accurately determining the drivers' activities. © 2021 Tech Science Press. All rights reserved.
About the journal
JournalData powered by TypesetComputers, Materials and Continua
PublisherData powered by TypesetTech Science Press
ISSN15462218