Skip to main navigation menu Skip to main content Skip to site footer
Español (España) | English
Editorial
Revistas / Journals
Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
Home
Issue
Current Issue Previous Issues Special Issue
About
About the Journal Editorial Team Privacy Statement Digital preservation policy Contact
Submissions
Author Guidelines Peer Review Process Ethical aspects Open Access Policy and Copyright Anti-plagiarism policy Crossmark policy page
Indexing
×
Español (España) | English
Editorial

Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
Home

Current Issue Previous Issues Special Issue

About the Journal Editorial Team Privacy Statement Digital preservation policy Contact

Author Guidelines Peer Review Process Ethical aspects Open Access Policy and Copyright Anti-plagiarism policy Crossmark policy page
Indexing
Original
Published: 2023-12-30
DOI: https://doi.org/10.56294/dm2023154

Pyramid Scene Parsing Network for Driver Distraction Classification

By
Abdelhak Khadraoui ,
Abdelhak Khadraoui

Moulay Ismail University, ENSAM, Meknes, Morocco

Search this author on:

PubMed | Google Scholar
Elmoukhtar Zemmouri ,
Elmoukhtar Zemmouri

Moulay Ismail University, ENSAM, Meknes, Morocco

Search this author on:

PubMed | Google Scholar

Abstract

In recent years, there has been a persistent increase in the number of road accidents worldwide. The US National Highway Traffic Safety Administration reports that distracted driving is responsible for approximately 45 percent of road accidents. In this study, we tackle the challenge of automating the detection and classification of driver distraction, along with the monitoring of risky driving behavior. Our proposed solution is based on the Pyramid Scene Parsing Network (PSPNet), which is a semantic segmentation model equipped with a pyramid parsing module. This module leverages global context information through context aggregation from different regions. We introduce a lightweight model for driver distraction classification, where the final predictions benefit from the combination of both local and global cues. For model training, we utilized the publicly available StateFarm Distracted Driver Detection Dataset. Additionally, we propose optimization techniques for classification to enhance the model’s performance.

Keywords:

Driver Distraction Detection,
Pyramid Scene Parsing Network,
Pspnet,
Statefarm’s Dataset,
Convolutional Neural Networks,

How to Cite

1.
Khadraoui A, Zemmouri E. Pyramid Scene Parsing Network for Driver Distraction Classification. Data and Metadata [Internet]. 2023 Dec. 30 [cited 2024 Jul. 27];2:154. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/154

Copyright Notice

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

Article metrics

Google scholar: See link

Metrics

Metrics Loading ...

Vol. 2 (2023)
See full issue

The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.