Driver Safety Development: Real-Time Driver Drowsiness Detection System Based on Convolutional Neural Network

Hashemi, Maryam; Mirrashid, Alireza; Shirazi, Ali Asghar Beheshti

doi:10.1007/s42979-020-00306-9

Cited by 68 publications

(26 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Detection of driver drowsiness with CNN Hashemi et al proposed a real-time DDD system based on the area of eye closure and use of the convolutional neural network (CNN) [50]. Three networks were introduced for eye closure classification: fully designed neural network (FD-NN), transfer learning in VGG16 (TL-VGG16), and transfer learning in VGG19 (TL-VGG19), with extra designed layers.…”

mentioning

confidence: 99%

A Review of Recent Developments in Driver Drowsiness Detection Systems

Albadawi

Takruri

Awad

2022

Sensors

View full text Add to dashboard Cite

Continuous advancements in computing technology and artificial intelligence in the past decade have led to improvements in driver monitoring systems. Numerous experimental studies have collected real driver drowsiness data and applied various artificial intelligence algorithms and feature combinations with the goal of significantly enhancing the performance of these systems in real-time. This paper presents an up-to-date review of the driver drowsiness detection systems implemented over the last decade. The paper illustrates and reviews recent systems using different measures to track and detect drowsiness. Each system falls under one of four possible categories, based on the information used. Each system presented in this paper is associated with a detailed description of the features, classification algorithms, and used datasets. In addition, an evaluation of these systems is presented, in terms of the final classification accuracy, sensitivity, and precision. Furthermore, the paper highlights the recent challenges in the area of driver drowsiness detection, discusses the practicality and reliability of each of the four system types, and presents some of the future trends in the field.

show abstract

mentioning

confidence: 99%

A Review of Recent Developments in Driver Drowsiness Detection Systems

Albadawi

Takruri

Awad

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…All this together was used to detect the drowsiness of the driver. Apart from this, Maryam Hashem et al 27) detected eyes out of the face using Viola Jones technique and cropped the image and chose one out of them. To overcome the challenge of the lighting condition, the authors used a histogram equalizer to equalize eye contrast.…”

Section: Fig 1: Classification Of Drowsiness Detection Techniquesmentioning

confidence: 99%

DDNet- A Deep Learning Approach to Detect Driver Distraction and Drowsiness

Panwar¹,

Roshan²,

Singh³

et al. 2022

Evergreen

View full text Add to dashboard Cite

Road accidents are the main cause of death among the human population. Distracted and drowsy driving takes thousands of lives every year around the world. Subsequently, to forestall such mishaps and save lives, there is a requirement for a system that detects both distraction and drowsiness for both day and night time. In this paper, we present a deep learning convolutional model to detect distraction and drowsiness during driving. The proposed model performs real-time video processing for monitoring the activities of drivers during driving. The model produces an alert in case of any careless driving or inappropriate behaviour of the driver with the minimum response time. For this purpose dataset for training as well as for testing were prepared. For training the model, we have used CNN model. The proposed model was able to achieve 99.95% accuracy on test dataset.

show abstract

“…They have successfully detected changes in facial expression at an 83.25% rate. [11] The difficulty of driver safety on the road is the main topic of this study, which also introduces a cuttingedge technology for drowsy driver detection. presented, one of which is a Fully Designed Neural Network (FD-NN), while the other two use Transfer Learning in VGG16 and VGG19 with additional designed layers (TL-VGG).…”

Section: Statistics Of Accident Due To Drowsiness In Indiamentioning

confidence: 99%

Survey: Driver Drowsiness Detection Systems

2022

IRJMETS

View full text Add to dashboard Cite

Car accidents are frequently caused due to many reasons like drowsiness, drunkenness or tiredness, which has serious consequences for traffic safety. If drowsy drivers were alerted in advance, many deadly accidents might be prevented. There are a number of drowsiness detection technologies that can be used to check for signs of inattention while driving and alert the driver. Self-driving cars must have sensors that can tell if a driver is drowsy, irritated, or going through dramatic swings in their emotions. Till now Many other kinds of warning systems have been suggested as additional drowsy driving prevention measures. These systems sound an alarm when they notice a major lane departure, a change in the headway distance, or other symptoms of poor driving performance. Other warning signs include increased head wobble or prolonged eye closure duration. These systems typically identify such behaviour after sleepy driving has started. This article offers many Driver Drowsiness Detection System applications for motor vehicles in perspective of this issue.

show abstract

Driver Safety Development: Real-Time Driver Drowsiness Detection System Based on Convolutional Neural Network

Cited by 68 publications

References 31 publications

A Review of Recent Developments in Driver Drowsiness Detection Systems

A Review of Recent Developments in Driver Drowsiness Detection Systems

DDNet- A Deep Learning Approach to Detect Driver Distraction and Drowsiness

Survey: Driver Drowsiness Detection Systems

Contact Info

Product

Resources

About