Driver Drowsiness and Yawn Detection with Speed based alerts

Syed Fuzail; Lakshmisha S Krishna; Asima Siddiqua; Syed Azam Hussain; Zubiya Sadaf

doi:10.36948/ijfmr.2024.v06i06.33719

Driver Drowsiness and Yawn Detection with Speed based alerts

Author(s)	Syed Fuzail, Lakshmisha S Krishna, Asima Siddiqua, Syed Azam Hussain, Zubiya Sadaf
Country	India
Abstract	The project, titled "Detection of Driver Drowsiness and Alertness," is one of the safety enhancement projects aimed at enhancing the safety of the roads by minimizing the risks associated with driver fatigue. Drowsiness affects the reaction time of a driver, his judgment ability, and general awareness levels and promotes a lot of accidents. It is the cause of many accidents caused by drowsy driving. This is an extreme problem that our project intends to solve by developing a web-based application that utilizes advanced machine learning algorithms and sensor data to assess the indicators of alertness and drowsiness while driving in real time. The device will reduce the chances of accidents associated with fatigue by alerting drivers through timely warnings and interventions to keep vigilance intact. The primary application is aimed at implementing computer vision and facial recognition algorithms to track critical markers of driver fatigue including head posture, blink rates, and eye movements. The system is supposed to analyze the physical condition and behavior of the driver by analyzing data from car cameras as well as sensors. It analyzes for things like head nodding , delayed eye blink closure, or several blinks in a short time frame. In the event that the system observes any signs of tiredness , it gives an alert to the driver using auditory or visual modes to take a break or do some form of restorative action to allow him or her to continue focus. Python and dlib are being utilized in the application's backend to prepare a learning model, which analyzes and trains data to improve its accuracy in the long term. The structure of the system is such that it is able to be integrated with modern in-vehicle technologies as it would operate in real time with minimal computation overhead. This project's main aim is to educate the people on the value of drivers' well-being with safety improvements.
Keywords	Python, dilib, Eye Aspect ratio, Mouth Aspect ratio, Speed based alerts, buzzer
Field	Engineering
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-24
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.33719
Short DOI	https://doi.org/g8w2wv

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Driver Drowsiness and Yawn Detection with Speed based alerts

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