Drowsiness detection in real-time via convolutional neural networks and transfer learning

Drowsiness detection in real-time via convolutional neural networks and transfer learning

Blog Article

Abstract Drowsiness detection is a critical aspect of ensuring safety in various domains, including transportation, online learning, and multimedia consumption.This research paper presents a comprehensive investigation into drowsiness detection methods, with a specific focus on utilizing convolutional neural networks (CNN) and spidertattooz.com transfer learning.Notably, the proposed study extends beyond theoretical exploration to practical application, as we have developed a user-friendly mobile application incorporating these advanced techniques.

Diverse datasets are integrated to systematically evaluate the implemented model, and the results showcase its remarkable effectiveness.For both multi-class and binary classification scenarios, our drowsiness detection system achieves impressive accuracy rates ranging from here 90 to 99.86%.

This research not only contributes to the academic understanding of drowsiness detection but also highlights the successful implementation of such methodologies in real-world scenarios through the development of our application.