SleepyWheels: An Ensemble Model for Drowsiness Detection leading to Accident Prevention

• URL: http://arxiv.org/abs/2211.00718v1
• Date: Tue, 1 Nov 2022 19:36:47 GMT
• Title: SleepyWheels: An Ensemble Model for Drowsiness Detection leading to Accident Prevention
• Authors: Jomin Jose, Andrew J, Kumudha Raimond, Shweta Vincent
• Abstract summary: SleepyWheels is a revolutionary method that uses a lightweight neural network in conjunction with facial landmark identification. The model is trained on a specially created dataset on driver sleepiness and it achieves an accuracy of 97 percent.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Around 40 percent of accidents related to driving on highways in India occur due to the driver falling asleep behind the steering wheel. Several types of research are ongoing to detect driver drowsiness but they suffer from the complexity and cost of the models. In this paper, SleepyWheels a revolutionary method that uses a lightweight neural network in conjunction with facial landmark identification is proposed to identify driver fatigue in real time. SleepyWheels is successful in a wide range of test scenarios, including the lack of facial characteristics while covering the eye or mouth, the drivers varying skin tones, camera placements, and observational angles. It can work well when emulated to real time systems. SleepyWheels utilized EfficientNetV2 and a facial landmark detector for identifying drowsiness detection. The model is trained on a specially created dataset on driver sleepiness and it achieves an accuracy of 97 percent. The model is lightweight hence it can be further deployed as a mobile application for various platforms.

Related papers

• Driver Drowsiness Detection System: An Approach By Machine Learning Application [0.0]
  A million people worldwide die each year due to traffic accident injuries. drowsiness becomes the main principle for to increase in the number of road accidents. This paper focus to resolve the problem of drowsiness detection with an accuracy of 80%.
  arXiv  Detail & Related papers  (2023-03-11T05:05:36Z)
• Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data [67.60224656603823]
  Sleep Activity Recognition methods can provide indicators to assess, monitor, and characterize subjects' sleep-wake cycles and detect behavioral changes. We propose a general method that continuously operates on passively sensed data from smartphones to characterize sleep and identify significant sleep episodes. Thanks to their ubiquity, these devices constitute an excellent alternative data source to profile subjects' biorhythms in a continuous, objective, and non-invasive manner.
  arXiv  Detail & Related papers  (2023-01-17T15:18:45Z)
• Modelling and Detection of Driver's Fatigue using Ontology [60.090278944561184]
  Road accidents are the eight leading cause of death all over the world. Various factors cause driver's fatigue. Ontological knowledge and rules for driver fatigue detection are to be integrated into an intelligent system.
  arXiv  Detail & Related papers  (2022-08-31T08:42:28Z)
• Drivers' attention detection: a systematic literature review [62.997667081978825]
  Many factors can contribute to distractions while driving, since objects or events to physiological conditions, as drowsiness and fatigue, do not allow the driver to stay attentive. The technological progress allowed the development and application of many solutions to detect the attention in real situations. Our work presents a Systematic Literature Review of the methods and criteria used to detect attention of drivers at the wheel.
  arXiv  Detail & Related papers  (2022-04-06T11:36:40Z)
• CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving [117.87070488537334]
  We introduce a challenging dataset named CODA that exposes this critical problem of vision-based detectors. The performance of standard object detectors trained on large-scale autonomous driving datasets significantly drops to no more than 12.8% in mAR. We experiment with the state-of-the-art open-world object detector and find that it also fails to reliably identify the novel objects in CODA.
  arXiv  Detail & Related papers  (2022-03-15T08:32:56Z)
• Detection of Driver Drowsiness by Calculating the Speed of Eye Blinking [0.0]
  We consider a simple real-time detection system for drowsiness based on the eye blinking rate. If the speed of the eye blinking drops below some empirically determined threshold, the system triggers an alarm. We find that this system works well if the face is directed to the camera, but it becomes less reliable once the head is tilted significantly.
  arXiv  Detail & Related papers  (2021-10-21T16:02:05Z)
• In-the-wild Drowsiness Detection from Facial Expressions [6.569756709977793]
  Driving in a state of drowsiness is a major cause of road accidents, resulting in tremendous damage to life and property. We propose a data collection protocol that involves outfitting vehicles of overnight shift workers with camera kits that record their faces while driving. We experiment with different convolutional and temporal neural network architectures to predict drowsiness states from pose, expression and emotion-based representation of the input video of the driver's face.
  arXiv  Detail & Related papers  (2020-10-21T17:28:56Z)
• Towards Evaluating Driver Fatigue with Robust Deep Learning Models [0.0]
  Drowsy driving results in approximately 72,000 crashes and 44,000 injuries every year in the US. We propose a framework to detect eye closedness in a captured camera frame as a gateway for detecting drowsiness.
  arXiv  Detail & Related papers  (2020-07-16T16:44:49Z)
• Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring [52.557003792696484]
  We present a framework for the detection of the drivers' intention based on both in-cabin and traffic scene videos. Our framework achieves a prediction with the accuracy of 83.98% and F1-score of 84.3%.
  arXiv  Detail & Related papers  (2020-06-20T11:56:32Z)
• Physically Realizable Adversarial Examples for LiDAR Object Detection [72.0017682322147]
  We present a method to generate universal 3D adversarial objects to fool LiDAR detectors. In particular, we demonstrate that placing an adversarial object on the rooftop of any target vehicle to hide the vehicle entirely from LiDAR detectors with a success rate of 80%. This is one step closer towards safer self-driving under unseen conditions from limited training data.
  arXiv  Detail & Related papers  (2020-04-01T16:11:04Z)
• Driver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application [0.8644909837301148]
  This article focuses on the detection of micro sleep and drowsiness using neural network based methodologies. accuracy was increased by utilizing facial landmarks which are detected by the camera and that is passed to a Convolutional Neural Network (CNN) to classify drowsiness. The proposed CNN based model can be used to build a real-time driver drowsiness detection system for embedded systems and Android devices with high accuracy and ease of use.
  arXiv  Detail & Related papers  (2020-01-17T12:39:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.