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Pyramid Scene Parsing Network for Driver Distraction Classification

By
Abdelhak Khadraoui ,
Abdelhak Khadraoui

Moulay Ismail University, ENSAM, Meknes, Morocco

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Elmoukhtar Zemmouri ,
Elmoukhtar Zemmouri

Moulay Ismail University, ENSAM, Meknes, Morocco

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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.

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 Apr. 24];2:154. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/154

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

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