Open Access

ARTICLE

Performance Comparison of Deep CNN Models for Detecting Driver’s Distraction

Kathiravan Srinivasan¹, Lalit Garg^2,*, Debajit Datta³, Abdulellah A. Alaboudi⁴, N. Z. Jhanjhi⁵, Rishav Agarwal³, Anmol George Thomas¹

1 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India
2 Faculty of Information and Communication Technology, University of Malta, Msida, 2080, MSD, Malta
3 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India
4 College of Computer Science, Shaqra University, Kingdom of Saudi Arabia
5 School of Computer Science and Engineering, SCE, Taylor’s University, Subang Jaya, 47500, Malaysia

* Corresponding Author: Lalit Garg. Email:

(This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)

Computers, Materials & Continua 2021, 68(3), 4109-4124. https://doi.org/10.32604/cmc.2021.016736

Received 10 January 2021; Accepted 22 March 2021; Issue published 06 May 2021

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 semi-automated 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 deep CNN models, namely the VGG16, 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.

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Citations