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Article

Deep Learning Approach Based on Residual Neural Network and SVM Classifier for Driver’s Distraction Detection

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School of Systems and Technology (SST), University of Management and Technology, UMT Road, C-II Johar Town, Lahore 54000, Pakistan
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College of Computer Science and Information Technology, University of Anbar, 11, Ramadi 31001, Iraq
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Lloyds Register Foundation Transport Risk Management Centre, Imperial College London, London SW7 2AZ, UK
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College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
*
Author to whom correspondence should be addressed.
Academic Editor: Feng Guo
Appl. Sci. 2022, 12(13), 6626; https://doi.org/10.3390/app12136626
Received: 18 May 2022 / Revised: 18 June 2022 / Accepted: 20 June 2022 / Published: 30 June 2022
In the last decade, distraction detection of a driver gained a lot of significance due to increases in the number of accidents. Many solutions, such as feature based, statistical, holistic, etc., have been proposed to solve this problem. With the advent of high processing power at cheaper costs, deep learning-based driver distraction detection techniques have shown promising results. The study proposes ReSVM, an approach combining deep features of ResNet-50 with the SVM classifier, for distraction detection of a driver. ReSVM is compared with six state-of-the-art approaches on four datasets, namely: State Farm Distracted Driver Detection, Boston University, DrivFace, and FT-UMT. Experiments demonstrate that ReSVM outperforms the existing approaches and achieves a classification accuracy as high as 95.5%. The study also compares ReSVM with its variants on the aforementioned datasets. View Full-Text
Keywords: residual network; convolution neural network; safe driving; distraction detection residual network; convolution neural network; safe driving; distraction detection
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MDPI and ACS Style

Abbas, T.; Ali, S.F.; Mohammed, M.A.; Khan, A.Z.; Awan, M.J.; Majumdar, A.; Thinnukool, O. Deep Learning Approach Based on Residual Neural Network and SVM Classifier for Driver’s Distraction Detection. Appl. Sci. 2022, 12, 6626. https://doi.org/10.3390/app12136626

AMA Style

Abbas T, Ali SF, Mohammed MA, Khan AZ, Awan MJ, Majumdar A, Thinnukool O. Deep Learning Approach Based on Residual Neural Network and SVM Classifier for Driver’s Distraction Detection. Applied Sciences. 2022; 12(13):6626. https://doi.org/10.3390/app12136626

Chicago/Turabian Style

Abbas, Tahir, Syed Farooq Ali, Mazin Abed Mohammed, Aadil Zia Khan, Mazhar Javed Awan, Arnab Majumdar, and Orawit Thinnukool. 2022. "Deep Learning Approach Based on Residual Neural Network and SVM Classifier for Driver’s Distraction Detection" Applied Sciences 12, no. 13: 6626. https://doi.org/10.3390/app12136626

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