Driver cellphone usage detection using wavelet scattering and convolutional neural networks

	Driver cellphone usage detection using wavelet scattering and convolutional neural networks
AUT Journal of Mathematics and Computing
مقاله 6، دوره 6، شماره 3، 2025، صفحه 257-268 اصل مقاله (1.96 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22060/ajmc.2023.22580.1177
نویسندگان
Ali Besharati؛ Ali Nahvi^* ؛ Serajeddin Ebrahimian
Virtual Reality Laboratory, K.N. Toosi University of Technology, Tehran, Iran
چکیده
This paper provides an automated system based on machine learning and computer vision to detect cellphone usage during driving. We used Wavelet Scattering Networks, which is a simple and efficient type of architecture. The pre[1]sented model is straightforward and compact and requires little hyper-parameter tuning. The speed of this model is similar to the Convolutional Neural Networks. We monitored the driver from two viewpoints: a frontal view of the driver’s face and a side view of the driver’s whole body. We created a new dataset for the first view[1]point, and used a publicly available dataset for the second viewpoint. Our model achieved the test accuracy of 91% for our new dataset and 99% for the publicly available one.
کلیدواژه‌ها
Mobile use detection؛ Wavelet scattering network؛ CNN؛ Cascade object detector؛ Transfer learning
مراجع

آمار تعداد مشاهده مقاله: 585 تعداد دریافت فایل اصل مقاله: 421