Real time distracted driver detection using Xception architecture and Raspberry Pi

Researchers are concentrating on developing technologies to identify and caution drivers against driving while distracted because it is a major cause of traffic accidents. According to the National Highway Traffic Safety Administrator's report, distracted driving is to blame for roughly one in every five car accidents.Our goal is to create an accurate and dependable method for identifying distracted drivers and alerting them to their lack of focus. We take inspiration from the success of convolutional neural networks in computer vision to do this. Our strategy entails putting in place a CNN-based system that can recognize when a driver is distracted as well as pinpoint the precise cause of their preoccupation. Real-time detection, however, necessitates three apparently mutually exclusive requirements for an optimal network: a small number of parameters, high accuracy, and fast speed.

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Publication Details

Journal
INTELIGENCIA ARTIFICIAL
Published
2024-10-19
DOI
https://doi.org/10.4114/intartif.vol28iss75pp15-29
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
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article

Real time distracted driver detection using Xception architecture and Raspberry Pi

Reji Mathew, P. Prajith, Uma Narayanan, Royal Alexandar et al.
INTELIGENCIA ARTIFICIAL
Network Security and Intrusion Detection
article

Real time distracted driver detection using Xception architecture and Raspberry Pi

Reji Mathew, P. Prajith, Uma Narayanan, Royal Alexandar, Vishnu Vikraman
article en

Abstract

Researchers are concentrating on developing technologies to identify and caution drivers against driving while distracted because it is a major cause of traffic accidents. According to the National Highway Traffic Safety Administrator's report, distracted driving is to blame for roughly one in every five car accidents.Our goal is to create an accurate and dependable method for identifying distracted drivers and alerting them to their lack of focus. We take inspiration from the success of convolutional neural networks in computer vision to do this. Our strategy entails putting in place a CNN-based system that can recognize when a driver is distracted as well as pinpoint the precise cause of their preoccupation. Real-time detection, however, necessitates three apparently mutually exclusive requirements for an optimal network: a small number of parameters, high accuracy, and fast speed.

INTELIGENCIA ARTIFICIALVol. 28(75)
Rajagiri Hospital (IN)
Openalex Percentile: Top 80%
Network Security and Intrusion Detection
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