International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 09 | Sep 2025
p-ISSN: 2395-0072
www.irjet.net
Driver Drowsiness Detection System Prof. Manikrao M.1, Azra Farheen2 1Professor, Dept. of Computer Science and Engineering, Guru Nanak Dev Engineering College, Karnataka, India 2Student, Dept. of Computer Science and Engineering, Guru Nanak Dev Engineering College, Karnataka, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Accidents happen during drowsy road trips and
set up to constantly monitor the driver's eyes to show their state. Early detection of driver fatigue can be achieved by observing the eyes, which can help prevent a minor collision. A series of images of a face can be used to measure the eye movements and squint patterns by the machine. Driver fatigue can be monitored using digital cameras, and the data collected can be utilized to construct frameworks in place. The main technique relies on facial recognition technology that examines a driver's facial expressions captured by cameras.
are becoming more frequent; it is a well-known fact that fatigue and distraction of the driver lead to numerous mishaps and injuries, and this research is primarily focused on increasing awareness of drowsiness. The main objective of drowsiness detection systems is to reduce these accidents. Previous studies on systems for detecting drowsiness were the focus of secondary data, and several methods were used to identify drowsiness or inattentive riding. Our motive is the creation of a system that can detect the driver's drowsiness and prevent accidents by analyzing the image of a person captured through the webcam and aiming at how this data can be used to enhance safety measures. This car protection mission aims to save you from injuries caused by the driver's sleepiness. Essentially, you are collecting human photographs from the internet camera stream and exploring how to improve safety using that information. This involves gathering snapshots from the live webcam stream and training the system to recognize the drowsiness of the driving force. Basically, this process involves the collection of the images from the live webcam and applying the machine learning algorithms to them to identify if the driver is drowsy or not. If the driver is drowsy a buzzer alarm is played, and the sound is increased to wake the driver up. Therefore, this utility goes beyond the effort of detecting drowsiness while driving. Face extraction, eye extraction with dlib.
2. OBJECTIVE The suggested paper complies with the Driver’s drowsiness detection technology which is designed to enhance vehicle safety by preventing accidents caused by sleepy drivers. The primary goal is to remind the driver to get a short break to avoid any mishap by signaling them using an alarm or buzzer if drowsy. The system continuously monitors the driver’s eye retina to identify the signs of drowsiness. The system identifies the bent head, closed or squinted eyes, and yawning as an indication of drowsiness. Upon detecting drowsiness, the system promptly alerts the driver through an audible buzzer or alarm. By minimizing accidents, this technology contributes to better traffic flow and overall road safety management. 3.SYSTEM ANALYSIS
Key Words: Driver Drowsiness Detection, Driver Monitoring, Road Safety, Convolutional Neural Network (CNN), Facial feature, Drowsy identification,Alarm
Existing System:
Research indicates that nearly 25% of severe traffic accidents are linked to driver fatigue, highlighting that drowsiness contributes to more crashes than incidents caused by driving under the influence. This study aims to implement a driver fatigue monitoring system through advanced image processing techniques. The drowsiness detection mechanism relies on vision-based technology, primarily using a compact camera focused on the driver’s face. This camera continuously monitors eye movements to identify signs of sleepiness. The three most general methods to detect driver’s drowsiness are: a) vehicle-based b) behavior- and c) physiological-based methods. Vehiclebased: The steering wheel movement, the accelerator of vehicle or pattern of vehicle brakes, vehicle’s speed, and deviation in position of lane are monitored continuously in the method which is based on vehicle. If there is any deviation in the values detected, it is considered as driver drowsiness. The sensors are not connected to the driver, and this measurement is nonintrusive. Behavior-based: Visual behavior like blinking of eye, closing of eye, yawning,
1.INTRODUCTION Numerous collisions can be attributed to driver fatigue. It is believed that around 1200 fatalities and 76,000 injuries annually are caused by exhaustion-related accidents, as per previous estimates. Numerous automobile accidents are caused by driver drowsiness and fatigue. A significant step in the subject of accident prevention systems is developing and maintaining effective technologies that can accurately detect or alert the driver before a disaster occurs. To avoid accidents caused by tiredness on the roads, certain actions must be implemented. With the advancement of technology and the use of cameras for filtering, we can prevent major catastrophes and alert drivers who are experiencing drowsiness through a fatigue detection system. This project aims to increase the model's popularity. A machine can be
© 2025, IRJET
|
Impact Factor value: 8.315
|
ISO 9001:2008 Certified Journal
|
Page 122