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Driver cellphone usage detection using wavelet scattering and convolutional neural networks | ||
AUT Journal of Mathematics and Computing | ||
مقاله 6، دوره 6، شماره 3، مهر 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 | ||
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