Driver Drowsiness Detection System

Project Title

Driver Drowsiness Detection System

Project Area of Specialization Artificial IntelligenceProject Summary

Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving.

Project Objectives

The objective of this Python project is to build a drowsiness detection system that will detect that a person’s eyes are closed for a few seconds. This system will alert the driver when drowsiness is detected, and also detect the yawn of driver and alert.

Project Implementation Method

Step 1 – Take image as input from a camera.

Step 2 – Detect the face in the image and create a Region of Interest (ROI).

Step 3 – Detect the eyes and yawn from ROI and feed it to the classifier.

Step 4 – Classifier will categorize whether eyes are open or closed and also detect yawn.

Step 5 – Calculate score to check whether the person is drowsy or not.

Benefits of the Project

A countless number of people drive on the highway day and night. Taxi drivers, bus drivers, truck drivers and people traveling long-distance suffer from lack of sleep. Due to which it becomes very dangerous to drive when feeling sleepy. So, to prevent these accidents we will build a system using Python, OpenCV, and Keras which will alert the driver when he feels sleepy.

Technical Details of Final Deliverable

We are going to use raspberry pi 4 with a webcame and small lcd inside a car or other vehicle, that will focus the driver's view and alert to the driver when he/she feels sleepy.

Final Deliverable of the Project HW/SW integrated systemCore Industry TransportationOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Life on LandRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	70000
raspberry pi 4 8gb ram	Equipment	2	18000	36000
raspberry pi lcd	Equipment	2	2500	5000
raspberry pi 4 camera module	Equipment	2	1000	2000
raspberry pi adaptor orignal 3A	Equipment	2	1500	3000
SD card class 10	Equipment	2	1000	2000
raspberry pi casing	Equipment	2	800	1600
fan	Equipment	2	100	200
heat sinks	Equipment	2	100	200
other much more will required after working actually	Equipment	2	10000	20000