International Research Journal of Engineering and Technology (IRJET) Volume: 10 Issue: 05 | May 2023
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
Driver Drowsiness Detection System using Google ML Kit Face Detection API and Flutter Supriya Kapase1, Rohan Hande2, Shubham Teke3, Yash Dhumane4, Prasad Rajhans5 Department of Information Technology, NBN Sinhgad School of Engineering, Ambegaon (Bk), Pune-411041, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------analysing the driver's facial features and detecting signs of Abstract - Driver drowsiness is a leading cause of road
drowsiness, such as drooping eyelids and eye blinking duration. The proposed system is reliable, accurate, and can be easily implemented on mobile devices, making it a practical solution for detecting driver drowsiness in real-time.
accidents, which can result in serious injury or even death. In this research paper, we propose a driver drowsiness detection system using Google ML Kit Face Detection API and Flutter, which can be implemented on mobile phones to detect driver drowsiness in real-time and provide an alert to prevent accidents caused by drowsy driving. The system monitors the driver's facial features and analyses them for signs of drowsiness using the front-facing camera of a mobile device.
1.1 Problem Statement Driver drowsiness is a significant problem that can cause accidents on the road, leading to injuries and fatalities. Research has shown that drowsy driving is responsible for a significant number of accidents worldwide. There is, therefore, a need for systems that can detect driver drowsiness and alert the driver to take necessary precautions. This paper addresses this problem by proposing a driver drowsiness detection system using Google ML Kit Face Detection API and Flutter, which can be implemented on mobile phones and provide an accurate and reliable detection of driver drowsiness.
This system was built with Flutter, a cross-platform framework for mobile app development. We have integrated the Google ML Kit Face Detection API, which provides facial features detection and tracking capabilities. When the system detects signs of drowsiness, it alerts the driver, preventing potential accidents. The proposed system is reliable, accurate, and can be easily implemented on mobile devices, making it a practical solution for detecting driver drowsiness in real-time.
Key Words: Driver drowsiness detection, Machine Learning, Google ML Kit Face Detection API, Flutter, road safety.
1.2 Need
1. INTRODUCTION
Help prevent accidents by alerting the driver when they are showing signs of drowsiness or fatigue.
Can detect signs of drowsiness in real-time, allowing for immediate intervention to prevent accidents.
Offers an easy-to-use and convenient solution for driver drowsiness detection that can be integrated into mobile applications.
Driving while drowsy is a serious problem that can lead to accidents on the road, resulting in injuries, fatalities, and property damage. According to research, drowsy driving is responsible for a significant number of accidents worldwide. Therefore, there is a need for a reliable and accurate driver drowsiness detection system that can alert the driver to take necessary precautions to prevent accidents caused by drowsy driving.
2. Literature Survey
In recent years, machine learning technologies have emerged as a powerful tool for developing driver drowsiness detection systems. Google ML Kit Face Detection API is one such technology that can be used for developing a driver drowsiness detection system. Additionally, the Flutter framework provides an efficient and intuitive way to develop mobile applications that can implement these detection systems.
In a previous article on the subject under consideration, the authors described relevant research in the field of driver drowsiness detection. Various Drowsiness Detection methods have been evaluated in numerous innovative papers. Currently, the following developed systems have been taken into consideration:
In this research paper, we propose a driver drowsiness detection system using Google ML Kit Face Detection API and Flutter, which can be implemented on mobile phones to detect driver drowsiness in real-time and provide an alert to prevent accidents caused by drowsy driving. The system works by
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