Drowsiness Detection Research Articles

Enhanced Driver Drowsiness Detection System using Deep Learning and Image Processing Techniques

Driver drowsiness remains a critical factor contributing to road accidents worldwide. To address this issue, we present an enhanced driver drowsiness detection system leveraging advanced deep learning and image processing techniques. Our research focuses on developing a robust and accurate model capable of detecting drowsiness indicators in real-time from driver facial images. We conducted a comprehensive review of existing literature on driver drowsiness detection systems and identified key challenges and opportunities in the field. Leveraging this knowledge, we propose a novel methodology that integrates convolutional neural networks (CNNs) for feature extraction and classification, coupled with sophisticated image processing algorithms for facial recognition and eye state analysis. We describe the experimental setup, data collection process, and model training procedures, followed by a detailed presentation of results and performance evaluation metrics. Our findings demonstrate significant improvements in drowsiness detection accuracy, with the proposed system achieving promising results in both laboratory and real-world driving scenarios. The implications of our research extend to the development of more effective driver assistance systems and the enhancement of road safety measures. We conclude by discussing future research directions and potential applications for advancing driver drowsiness detection technology. Keywords— Driver Drowsiness Detection, Deep Learning, Image Processing, Convolutional Neural Networks (CNNs), Facial Recognition, Road Safety, Real-Time Monitoring, Driver Assistance Systems, Accident Prevention

Project Awakesure: Intelligent Drowsiness Detection Using Eye Tracking

Being sleepy or drowsy is referred to as being drowsy. A person who is sleepy may feel exhausted or lethargic and struggle to stay awake. People who are sleepy tend to be less attentive and may even nod off, though they can still be awakened. An increasing number of vocations nowadays call for sustained focus. In order for drivers to respond quickly to unexpected incidents, they must maintain a watchful eye on the road. Many road incidents are directly caused by tired drivers. In order to drastically lower the frequency of fatigue-related auto accidents, it is crucial to develop technologies that can identify and alert a driver to a poor psychophysical state. However, there are many challenges in developing systems that can quickly and accurately recognize a driver's signs of fatigue. Using vision-based technology is one technological option for implementing driver fatigue monitoring systems. The available driver drowsiness detection systems are described in this article. Here, we are assessing the driver's level of sleepiness utilizing his visual system. The automated system for preventing accidents and monitoring sleepy drivers developed for this study is based on detecting variations in the length of eye blinks. Our recommended technique makes use of the eyes' postulated horizontal symmetry property to identify visual changes in eye positions. Our novel approach precisely positions a standard webcam in front of the driver's seat to identify eye blinks. It will identify the eyeballs based on a specific EAR (Eye Aspect Ratio).

Real-time smart driver sleepiness detection by eye aspect ratio using computer vision

The purpose of this study is to determine the optimal eye aspect ratio (EAR) for a prototype capable of using computer vision techniques to detect driver sleepiness based on eyelid size changes. The prototype, developed with Raspberry Pi and OpenCV, provides a real-time evaluation of the driver's level of alertness. The prototype can accurately determine the onset of sleepiness by monitoring and detecting instances of prolonged eyelid closure. Due to the fact that the eye aspect ratios of different individuals vary in size, the system's accuracy may be compromised. For the first experiment, the research focuses on determining the optimal EAR threshold of the proposed prototype using a sample of 20 participants ranging in age from 20 to 30, 31 to 40, and 41 to 50 years old. The study also examines the effects of various environmental conditions, such as dark or nighttime settings and the use of spectacle. The optimal EAR threshold value, as dedicated by the first experiment, is 0.225 after testing 20 participants with and without eyeglasses in low and bright lighting and 7 participants with a 0.225 EAR threshold in dark and bright lighting environments. The result shows 100% precision.

Driving Alertness System Using Deep Learning

Abstract: Driver fatigue, a major contributor to road accidents, poses a significant challenge to road safety. Numerous fatal collisions could be prevented by promptly alerting drivers experiencing drowsiness. Several drowsiness detection methods have been developed to monitor driver alertness while driving and issue warnings when attention lapses. These systems employ various techniques to assess drowsiness levels, including extracting key features from facial expressions such as yawning, eye closure, and head movements. Evaluating both the driver's physiological state and vehicle behavior is crucial for identifying signs of drowsiness. This research delves into a comprehensive evaluation of prevalent classification methodologies and a thorough examination of contemporary driver drowsiness detection technologies. The study initially categorizes existing techniques into three broad groups: physiological, vehicular, and behavioral parameter-based approaches. It then provides an extensive review of recognized supervised learning algorithms employed for drowsiness detection. Furthermore, the examination conducts a comparative analysis of the advantages and limitations of various methods.

Alcohol and Drowsiness Detection in Smart Transportation System

Abstract: Drunk driving is a major contributing factor in the rising number of unintentional deaths that occur in the modern day. Drunk driving is an extremely dangerous practice that can result in car accidents and injuries on the road. Numerous preventative actions had been implemented, including license suspension, fines, and the seizure of motor vehicle permits. Notwithstanding the numerous measures implemented, the proportion of collisions caused by intoxicated drivers continues to rise. The purpose of this module is to shield people from needless deaths brought on by intoxicated and sleep-deprived drivers in traffic accidents. This module includes a Wi-Fi module and an alcohol detection sensor (MQ-3) based on the Raspberry Pi model 3. We are incorporating an automated drowsy driver monitoring and accident prevention system as part of the design of our major project.

Advancements in Drowsiness Detection: A Review

This review study looks at current advancements in sleepiness detection technologies to address the urgent problem of driving while sleepy leading to accidents. In order to prevent future accidents, it analyzes several approaches and technology and emphasizes how crucial it is to correctly recognize drowsy conditions in drivers. Important results highlight the effectiveness of methods for real-time drowsiness detection such as convolutional neural networks (CNNs), eye aspect ratio (EAR) analysis, and deep learning integration. Additionally, improvements in computer vision algorithms make it possible to precisely analyze facial landmarks and eye blinking patterns, which improves the accuracy of detection systems. In order to protect driver comfort and system reliability, the evaluation emphasizes the importance of non-intrusive fatigue detection techniques while also emphasizing the necessity to reduce false warnings. Remarkably, certain models detect drowsy-eye with an accuracy of above 95%. All things considered, the review emphasizes how important cutting-edge technology is in reducing the risks related to drunk driving, especially deep learning and computer vision algorithms. It promotes the deployment of reliable sleepiness detection technologies in order to increase road safety and reduce accidents brought on by fatigued drivers. Key Words: Drowsiness detection, Eye blink duration, Facial landmark detection, Deep learning algorithms, Computer vision, Eye aspect ratio, Road safety.

A survey on drowsiness detection system with advanced face tracking

To address the increasing dangers associated with driver and worker fatigue, this project introduces an advanced Drowsiness Detection System featuring state-of-the-art face-tracking capabilities. The pressing need for fatigue detection is evident in the alarming figures of 800 annual fatalities and 50,000 injuries resulting from drowsy driving incidents. This research expands the application of the technology to industrial workplaces, where the consequences of drowsiness are equally severe. Our comprehensive approach involves real-time monitoring of facial features, with a focus on eye movements and eyelid patterns. This system goes beyond traditional boundaries, covering drivers and industrial workers operating heavy machinery. By incorporating facial landmarks and introducing the innovative Eyes Aspect Ratio parameter, our technology offers a precise assessment of weariness in individuals within the current frame. This approach enhances safety measures in smart transportation systems and industrial settings. Integrating facial landmarks allows for a nuanced understanding of fatigue, recognizing subtle changes in facial expressions and movements indicative of drowsiness. The Eyes Aspect Ratio parameter, a novel addition, improves weariness assessment precision by considering factors such as eye closure duration, blink frequency, and gaze direction. These outcomes signify a significant contribution to road safety and broader workplace security. By mitigating inherent risks associated with drowsiness among industrial workers, our technology aims to reduce accidents, prevent injuries, and save lives across various occupational settings. The potential impact extends beyond individuals, influencing organizational safety protocols and contributing to the overall well-being of workers in high-risk environments.

Driver Identification and Detection of Drowsiness while Driving

This paper introduces a cutting-edge approach that combines facial recognition and drowsiness detection technologies with Internet of Things capabilities, including 5G/6G connectivity, aimed at bolstering vehicle security and driver safety. The delineated two-phase project is tailored to strengthen security measures and address accidents stemming from driver distraction and fatigue. The initial phase is centered on facial recognition for driver authentication before vehicle initiation. Following successful authentication, the subsequent phase harnesses continuous eye monitoring features, leveraging edge computing for real-time processing to identify signs of drowsiness during the journey. Emphasis is placed on video-based identification and analysis to ensure robust drowsiness detection. Finally, the study highlights the potential of these innovations to revolutionize automotive security and accident prevention within the context of intelligent transport systems.

Enhanced Driver Drowsiness Detection Solution with CNN Algorithm Integration

Enhanced Driver Drowsiness Detection Solution with CNN Algorithm Integration

Stay Alert: Drowsiness Detection with IoT Technology

The "Drowsiness Detection using IoT with Eye Blink Sensor and Buzzer" project aims to address the critical issue of driver fatigue and drowsiness, a major cause of road accidents. Leveraging Internet of Things (IoT) technologies and a combination of eye blink sensors and a buzzer, this system provides a real-time solution for monitoring and alerting drivers when signs of drowsiness are detected. The project focuses on enhancing road safety by preventing accidents caused by driver fatigue Drowsiness detection is a critical aspect of ensuring safety, particularly in domains such as transportation where fatigue-related accidents pose significant risks. In recent years, the proliferation of Internet of Things (IoT) devices has enabled innovative approaches to drowsiness detection systems. This abstract outlines a novel framework for drowsiness detection utilizing IoT technologies. Our proposed system integrates various sensors, including facial recognition cameras, heart rate monitors, and accelerometers, into a networked IoT infrastructure. These sensors continuously monitor key physiological and behavioral indicators associated with drowsiness, such as eye movement patterns, blink frequency, heart rate variability, and head position changes.

Design, Simulation, Building, and Testing of a Microcontroller-Based Automatic Drowsiness Detection, Vehicle Braking, and Alert System

Aims: The premier practical aspect of this research drive is to propose, build, simulate, and evaluate an Arduino-built automatic vehicle driver drowsiness detection and alert system.
 Study Design: The pivotal role of a reliable braking system in vehicular safety cannot be overstated, particularly considering the escalating frequency of traffic accidents, notably prevalent in Indonesia where human factors take center stage as the primary accident catalyst. An insightful poll underscores physical fatigue or drowsiness while driving as the foremost concern. Acknowledging the nuanced disparities between conventional air systems and electric systems, this research strategically directs its attention toward crafting a new system characteristic that mirrors the efficiency of the existing systems.
 Place and Period of Study: The research action engaged by the authors in a bunch of two students under the command of a professor as a part of one of his course capstone projects for the Bachelor of Science in Electrical and Electronic Engineering degree at the American International University Bangladesh (AIUB), Dhaka, Bangladesh. The authors performed their investigative tasks at AIUB from June 2023 to January 2024.
 Methodology: Recognizing the imperative to fortify vehicular safety, the integration of artificial intelligence emerges as a vital solution to assist drivers in ensuring the safety of individuals both within and outside the vehicle. This focused study, tailored specifically for Electric Vehicles (EVs), centers on the innovative application of object and distance identification methodologies to provide a comprehensive braking action indicator. Leveraging a minicomputer and a sophisticated neural network approach, images captured by a stereo camera undergo meticulous machine learning processes. This facilitates the precise categorization and measurement of distances between objects, with subsequent priority decisions determining the optimal degree of braking action. Employing an intelligent methodology, specifically fuzzy logic, the study demonstrates a successful outcome by constructing a curve while concurrently enhancing the dynamics of the pre-existing system. Moreover, a finely tuned Pulse Width Modulation (PWM) signal for braking, lasting 10 milliseconds, is intricately devised based on the study's discerning results.
 Results: The system is simulated in Proteus and tested in real time to check its functionality. The outcomes of the test showed that the system can produce the appropriate signals based on the detection of any kind of driver’s drowsiness and can stop the car.
 Conclusion: This comprehensive approach ensures the seamless replacement of components while concurrently elevating the overall performance of the braking system

Streamlined Workforce Management System Using AI

In today's digital landscape, face recognition technology has emerged as a versatile and non-invasive tool used across sectors, despite its lower accuracy compared to iris or fingerprint recognition. Its applications range from security and authentication to optimizing attendance systems in educational institutions and workplaces. This paper introduces an innovative class attendance system employing face recognition technology, replacing manual processes prone to inaccuracies like proxy attendance. The system involves database creation from student images, utilizing Haar-Cascade classifiers for face detection, and Local Binary Pattern Histogram algorithms for live recognition from classroom video streams. It automates attendance data distribution to faculty members, enhancing efficiency and precision. Simultaneously, the security industry necessitates alert and vigilant security personnel to prevent breaches. A sophisticated hybrid system, leveraging machine learning techniques like OpenCV and EAR/MAR algorithms alongside UHD cameras, detects guard drowsiness and alerts them promptly using sound cues and preventive measures, ensuring continual vigilance. This innovative hybrid system fortifies security guard surveillance, mitigating potential vulnerabilities due to lapses in attentiveness. Key Word: Face Recognition Technology, Biometrics, Attendance Management, Security, Authentication, Surveillance, Machine Learning, Open CV, Security Guard Monitoring, Vigilance, Drowsiness Detection.

Survey Paper on the Driver Drowsiness Detection using IoT

Traffic accidents caused by drowsy drivers represent a crucial threat to public safety. Recent statistics show that drowsy drivers cause an estimated 15.5% of fatal accidents. With the widespread use of mobile devices and roadside units, these accidents can be significantly prevented using a drowsiness detection solution. This device would be a valuable tool for helping to keep drivers safe on the road. It would be especially beneficial for drivers who are prone to drowsiness, such as long-haul truck drivers or shit workers. The system works by collecting data from the various sensors and using a machine learning algorithm. While several solutions were proposed in the literature, they all fall short of presenting a distributed architecture that can answer the needs of these applications without breaching the driver’s privacy.

Deep Learning-Based Attention Mechanism for Automatic Drowsiness Detection Using EEG Signal

Deep Learning-Based Attention Mechanism for Automatic Drowsiness Detection Using EEG Signal

Towards a system for real-time prevention of drowsiness-related accidents

<p>Traffic accidents always result in great human and material losses. One of the main causes of accidents is the human factor, which usually results from driver’s fatigue or drowsiness. To address this issue, several methods for predicting the driver’s state and behavior have been proposed. Some approaches are based on the measurement of the driver’s behavior such as: head movement, blinking time, mouth expression note, while others are based on physiological measurements to obtain information about the internal state of the driver. Several works used machine learning/deep learning to train models for driver behavior prediction. In this paper, we propose a new deep learning architecture based on residual and feature pyramid networks (FPN) for driver drowsiness detection. The trained model is integrated into a system that aims to prevent drowsinessrelated accidents in real-time. The system can detect drivers’ drowsiness in real time and alert the driver in case of danger. Experiment results on benchmarking datasets shows that our proposed architecture achieves high detection accuracy compared to baseline approaches.</p>

Research on Driver Drowsiness Detection Method based on Deep Learning

Research on Driver Drowsiness Detection Method based on Deep Learning

Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems

Currently, the percentage of traffic accidents has increased, and according to statistics, this percentage will continue to increase every year, so it is necessary to develop new technologies to prevent this kind of accidents. This paper presents a drowsiness detection system based on electroencephalogram (EEG) signals using a pair of channels (Fp1 and Fp2) applied to drivers before entering their vehicles. First, this model detects the relationship between the area under the curve (AUC) of alpha brain waves, an effective parameter for detecting drowsiness. Then, the extracted information is passed to a fuzzy expert system (FES) that classifies the subject's state as "alert" or "sleepy"; the criterion used was a threshold and training with subjective levels. The proposed system was compared with neural network models, such as support vector machine (SVM), K nearest neighbors (KNN), and random forest (RF). Measurements of one hundred and twenty minutes were performed on each of the ten drivers for two days to test the system. The tests confirm that this system is suitable for preventive measures and that the fuzzy system is superior to traditional neural network methods.

Enhancing Road Safety: Deep Learning-Based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems

Driver drowsiness detection is a significant element of Advanced Driver-Assistance Systems (ADASs), which utilize deep learning (DL) methods to improve road safety. A driver drowsiness detection system can trigger timely alerts like auditory or visual warnings, thereby stimulating drivers to take corrective measures and ultimately avoiding possible accidents caused by impaired driving. This study presents a Deep Learning-based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems (DLID3-ADAS) technique. The DLID3-ADAS technique aims to enhance road safety via the detection of drowsiness among drivers. Using the DLID3-ADAS technique, complex features from images are derived through the use of the ShuffleNet approach. Moreover, the Northern Goshawk Optimization (NGO) algorithm is exploited for the selection of optimum hyperparameters for the ShuffleNet model. Lastly, an extreme learning machine (ELM) model is used to properly detect and classify the drowsiness states of drivers. The extensive set of experiments conducted based on the Yawdd driver database showed that the DLID3-ADAS technique achieves a higher performance compared to existing models, with a maximum accuracy of 97.05% and minimum computational time of 0.60 s.

Eye Blink Detection- A New System for Driver Drowsiness and Distraction Detection

The advancement of technology has led to innovative solutions for ensuring road safety, particularly in the detection of driver drowsiness and distraction. This abstract presents a novel approach using eye blink detection as a fundamental component in a system designed to mitigate the risks associated with drowsy and distracted driving. The proposed system integrates cutting-edge computer vision techniques to accurately detect and analyze eye blinks in real-time. By utilizing high-resolution cameras installed within the vehicle, the system captures and processes images of the driver's face, focusing on the eyes to track blinking patterns and durations. Through sophisticated algorithms and machine learning models, it distinguishes between normal blinks and extended periods of eye closure, indicative of drowsiness or distraction. Furthermore, the system incorporates contextual awareness by considering various factors such as environmental conditions, driving behavior, and facial expressions to enhance the accuracy of detection. Machine learning algorithms trained on diverse datasets enable the system to adapt and improve its performance over time, making it more adept at recognizing subtle variations in blink patterns unique to each driver. The applications of this system are multifaceted. Firstly, it serves as an early warning system, alerting drivers when signs of drowsiness or distraction are detected. This proactive approach aims to prevent accidents by prompting drivers to take necessary breaks or refocus their attention on the road. Moreover, the system can integrate with existing driver assistance technologies, allowing for automated interventions, such as adjusting seat vibrations or emitting audible alerts to re-engage the driver. In conclusion, the proposed eye blink detection system represents a significant advancement in the realm of driver safety technologies. By leveraging sophisticated algorithms and real-time analysis of blink patterns, it offers a promising solution to mitigate the risks associated with driver drowsiness and distraction, ultimately contributing to safer roads and reducing the likelihood of accidents. KEYWORDS: Eye blinking, Detection, Driver, Drowsiness, Distraction, Detection, computer vision, machine learning, Deep learning, image processing.

Physiological and Physical Sensors for Stress Level, Drowsiness Detection, and Behaviour Analysis

Physiological and Physical Sensors for Stress Level, Drowsiness Detection, and Behaviour Analysis