Title: A novel approach to detect phone usage of motor-vehicle drivers by balancing image quality on roads

Authors: Mallikarjun Anandhalli; Pavana Baligar; Vishwanath P. Baligar; Srijan Bhattacharya

Addresses: Department of Electronics and Communication Engineering, Central University of Karnataka, India ' Department of Information Science and Engineering, S.K.S.V.M. Agadi College of Engineering and Technology, Laxmeshwar, India ' School of Computer Science and Engineering, KLE Technological University, Karnataka, India ' Department of Applied Electronics and Instrumentation Engineering, RCC Institute of Information Technology, Kolkata, India

Abstract: Mobile phone usage during driving is identified as one of the major causes of traffic accidents as it distracts the driver, mainly during driving the motorcycle. In this article authors are focused on detection of mobile phone usage during motorcycle driving. It has been observed that limited research work has been done in this domain due to the lack of ready datasets, occlusion of object (mobile phone), rotation and difficulty in extracting the features object. The authors collected the data in different Indian traffic conditions and applied convolutional neural network (CNN), deep learning-based YOLOv4 architecture with CSPDarknet-54 as the backbone of YOLOv4 algorithm. The results show the detection of mobile phone usage in traffic with a precision of 94%.

Keywords: mobile detection; YOLOv4; SERB-GIT dataset.

DOI: 10.1504/IJAPR.2023.130508

International Journal of Applied Pattern Recognition, 2023 Vol.7 No.2, pp.79 - 99

Received: 15 Nov 2021
Accepted: 16 May 2022
Published online: 25 Apr 2023 *

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