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.
