Jul 6, 2021 · This article presents a solution for driver drowsiness detection using a Convolutional Neural Network. The implementation of the project uses a custom CNN architecture with less than...

This project aimed to develop a system that could detect drowsiness in drivers based on their eye movements. The system uses a deep learning model, ResNet50, to analyze the driver's eye movements and classify them as either open or closed.

Sep 24, 2024 · This work includes analyzing CNN and computer vision models for eye detection, yawn detection, and head movement. The CNN models are trained by using MRL and YawDD datasets for eye and...

Jun 21, 2023 · Figure 6: Flow chart illustration of driver drowsiness seat belt and distraction system. Fig. 6, shown in the form of a flow chart how our work will be done regarding the detection of driver drowsiness or yawn detections and seat belt detections starting with when driver video will be captured. This paper has discussed a novel approach.

the implementation of a Driver Drowsiness Detection System using Convolutional Neural Networks (CNN) and Haar Cascade algorithms. The project utilizes OpenCV, Keras, TensorFlow, and NumPy for real-time eye monitoring and detection of drowsiness.

This project aims to prevent and reduce such accidents by creating a drowsiness detection agent. Here, we used Python, OpenCV, and Keras to build a system that can detect the closed eyes of drivers and alert them if ever they fall asleep while driving.

The proposed method for drowsy driver detection using convolutional neural networks and Viola-Jones Haar-like features involves extracting frames from a video and feeding them into a face detector. The

This study proposes a DrowsyDetectNet that utilizes a shallow Convolutional Neural Network (CNN) architecture to identify driver drowsiness. The 68-point face landmark identification approach is used to identify faces and extract eye areas.

In this research work, a CNN model will be built to identify the drowsiness of the driver by observing the driver's face reactions. The driver drowsiness dataset is downloaded from data-flair website and trained with CNN architecture.

Driver Drowsiness is among the primary reasons for road accidents. It poses a potential threat to the drivers, passengers and individuals involved in traffic. To address this critical issue, this paper explores the efficacy of driver drowsiness detection using deep learning (DL) and machine learning (ML) techniques to develop robust systems that enhance road safety. Transfer learning is ...