International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 08 | Aug 2026
p-ISSN: 2395-0072
www.irjet.net
Driver Drowsiness Detection System for Accident Prevention System Mandar Anil Dumbre Department of Computer Science, Fergusson College (Autonomous), Pune, Maharashtra, India ----------------------------------------------------------------------------***----------------------------------------------------------------------------We are presenting techniques to detect driver drowsiness Abstract - Driver fatigue is a major safety concern using OpenCV, raspberry pi and image processing. because reduced alertness can affect a driver's ability to respond to road conditions. This paper presents a driver Several studies have shown various possible techniques drowsiness detection and accident prevention system using that can detect the driver's drowsiness. Such drowsiness a Raspberry Pi, Pi Camera, OpenCV and image processing can be measured or detected using physiological, ocular techniques. The proposed system continuously captures the and performance monitoring. Among these, monitoring driver's facial activity and detects the face and eyes using physiological and ocular changes can give more accurate facial landmark based processing. Eye Aspect Ratio (EAR) is results. Detecting and tracking physiological changes like used to identify prolonged eye closure and determine changes in brain waves, heart rate, pulse rate, etc require possible drowsiness. When the EAR falls below the defined attaching sensors physically to the driver such as threshold for a sufficient number of frames, the system connecting electrodes to the driver body. This leads to activates an audible warning and sends an alert message. uncomfortable driving conditions. But ocular changes can The system also incorporates GPS based location tracking be measured without physical connection. Ocular and collision sensing so that information about an accident detection of the driver's eye movements and possible can be forwarded to an authorized person or nearby vision based on eye closure is well-suited for real world assistance. The prototype combines real time computer driving conditions, since it can detect the eyes remaining vision, embedded processing, sensing and communication to open / closed non-intrusively using a camera. provide a cost effective approach for driver monitoring and accident response. The reported testing involved ten individuals under different conditions and the system Additionally, the project will demonstrate the capabilities achieved a reported accuracy of 97.1 percent. of the Raspberry Pi computer and how it can be used to create innovative solutions to the real-world problem of Key Words: Driver Drowsiness Detection, Raspberry driver drowsiness. Pi, OpenCV, Eye Aspect Ratio, Computer Vision, GPS, Accident Prevention 2. LITERATURE REVIEW
1. INTRODUCTION
Drowsiness detection can be carried out by two techniques. The first technique is intrusive and second is nonintrusive. The intrusive technique involves computation of mind wave monitoring, heart-beat rate etc. Non-invasive techniques are appropriate to find facial appearance for tiredness detection. Mouth gaping and Eye closure are the wellknown symptoms of the drowsiness detection [2]. The nonintrusive technique involves head pose, eye blinking rate, yawn detection, eye closure, etc., [1]. Another non-invasive way to detect fatigue can be divided into three scenarios: visual cues, physiological measurements, driving performance. Physiological and visual cues involve direct computation, whereas driving performance involves indirect computation [3]. It is suitable for the real-time application, because of no need for sensing electrodes.
The theme of the project is "Driver Drowsiness Detection System for Accident Prevention System using Raspberry Pi." The goal of the project is to create a system that can detect if a driver is getting drowsy or falling asleep while operating a vehicle. This system will use a Raspberry Pi computer to monitor the driver's behaviour and alert them if they are showing signs of fatigue. The project will involve using various sensors and machine learning algorithms to detect changes in the driver's behaviour, such as changes in facial expressions, eye movements, and head position. The Raspberry Pi will process this data in real-time and issue an alert if it detects that the driver is getting drowsy or falling asleep.
1. Haar cascade classifier
The main objective of this project is to reduce the number of accidents caused by driver fatigue. Real time drowsy driving detection is one of the best possible measures that can be implemented to assist drivers to make them aware of drowsy driving conditions. Such driver behavioural state detection systems can help in catching the drowsy driver conditions early and can possibly avoid mishaps.
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