EARLY DRIVER FATIGUE DETECTION SYSTEM

Abstract

The main issue in modern transportation is road safety, hence this endeavor aims to alleviate driver weariness. The goal is to use the versatile machine learning framework Dlib and the tiny single-board computer Raspberry Pi to create a sophisticated, reasonably priced driver fatigue detection system. The major goals include early warnings to prevent possible crashes, lower deployment costs, improved system reliability, and real-time detection of driver fatigue signs. The process uses Dib’s powerful facial landmark identification algorithm, machine learning methods to identify exhaustion, and face data collection and analysis using the Raspberry Pi camera. The system is shown to be valuable in improving traffic safety and reducing the hazards related to driver weariness by means of rigorous testing and assessment procedures that validate its accuracy, dependability, and financial sustainability. The project considers system resilience and economic sustainability while also providing a workable solution for raising driver safety and lowering traffic accidents to create intelligent transportation networks. By means of creative application of accessible technologies, this project aims to tackle a pressing social issue and greatly enhance automobile safety.