Vision Transformers and YoloV5 based Driver Drowsiness Detection Framework

by   Ghanta Sai Krishna, et al.

Human drivers have distinct driving techniques, knowledge, and sentiments due to unique driving traits. Driver drowsiness has been a serious issue endangering road safety; therefore, it is essential to design an effective drowsiness detection algorithm to bypass road accidents. Miscellaneous research efforts have been approached the problem of detecting anomalous human driver behaviour to examine the frontal face of the driver and automobile dynamics via computer vision techniques. Still, the conventional methods cannot capture complicated driver behaviour features. However, with the origin of deep learning architectures, a substantial amount of research has also been executed to analyze and recognize driver's drowsiness using neural network algorithms. This paper introduces a novel framework based on vision transformers and YoloV5 architectures for driver drowsiness recognition. A custom YoloV5 pre-trained architecture is proposed for face extraction with the aim of extracting Region of Interest (ROI). Owing to the limitations of previous architectures, this paper introduces vision transformers for binary image classification which is trained and validated on a public dataset UTA-RLDD. The model had achieved 96.2% and 97.4% as it's training and validation accuracies respectively. For the further evaluation, proposed framework is tested on a custom dataset of 39 participants in various light circumstances and achieved 95.5% accuracy. The conducted experimentations revealed the significant potential of our framework for practical applications in smart transportation systems.


page 1

page 3

page 4

page 5

page 7

page 9


Improved YOLOv3 Object Classification in Intelligent Transportation System

The technology of vehicle and driver detection in Intelligent Transporta...

Deep Learning Approach for Aggressive Driving Behaviour Detection

Driving behaviour is one of the primary causes of road crashes and accid...

A Computer Vision-Based Approach for Driver Distraction Recognition using Deep Learning and Genetic Algorithm Based Ensemble

As the proportion of road accidents increases each year, driver distract...

Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks

Driver drowsiness increases crash risk, leading to substantial road trau...

Applying Spatiotemporal Attention to Identify Distracted and Drowsy Driving with Vision Transformers

A 20 result of increased distraction and drowsiness. Drowsy and distract...

A Deep-Learning Framework to Predict the Dynamics of a Human-Driven Vehicle Based on the Road Geometry

Many trajectory forecasting methods, implementing deterministic and stoc...

Assessment of Vehicular Vision Obstruction Due to Driver-Side B-Pillar and Remediation with Blind Spot Eliminator

Blind spots created by the driver-side B-pillar impair the ability of th...

Please sign up or login with your details

Forgot password? Click here to reset