A novel approach to detect phone usage of motor-vehicle drivers by balancing image quality on roads
by Mallikarjun Anandhalli; Pavana Baligar; Vishwanath P. Baligar; Srijan Bhattacharya
International Journal of Applied Pattern Recognition (IJAPR), Vol. 7, No. 2, 2023

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%.

Online publication date: Tue, 25-Apr-2023

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Applied Pattern Recognition (IJAPR):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com