Safety is one of the most important priorities of the rail transport industry. Train driver sleepiness is a major threat to railway safety as it can lead to accidents and irreparable losses. In recent years, artificial intelligence and machine learning have emerged as promising approaches for developing driver Drowsiness detection strategies. In this article, both deep learning (DL) and convolutional neural network (CNN) approaches are used, and methods to detect train driver Drowsiness using YOLOv7 and YOLOv8 models are presented. YOLOv7 and YOLOv8 are the most recent object detection models that are effective for various tasks, including driver Drowsiness detection. Our studies show that YOLOv8 outperforms YOLOv7 in terms of accuracy, speed of processing and learning, and required memory in detecting train driver Drowsiness. Real-time detection features using YOLOv8 models are also demonstrated. These features can be used to detect Drowsiness in real time, which can help prevent accidents.Safety is one of the most important priorities of the rail transport industry. Train driver sleepiness is a major threat to railway safety as it can lead to accidents and irreparable losses. In recent years, artificial intelligence and machine learning have emerged as promising approaches for developing driver Drowsiness detection strategies. In this article, both deep learning (DL) and convolutional neural network (CNN) approaches are used, and methods to detect train driver Drowsiness using YOLOv7 and YOLOv8 models are presented. YOLOv7 and YOLOv8 are the most recent object detection models that are effective for various tasks, including driver Drowsiness detection. Our studies show that YOLOv8 outperforms YOLOv7 in terms of accuracy, speed of processing and learning, and required memory in detecting train driver Drowsiness. Real-time detection features using YOLOv8 models are also demonstrated. These features can be used to detect Drowsiness in real time, which can help prevent accidents.

Safety is one of the most important priorities of the rail transport industry. Train driver sleepiness is a major threat to railway safety as it can lead to accidents and irreparable losses. In recent years, artificial intelligence and machine learning have emerged as promising approaches for developing driver Drowsiness detection strategies. In this article, both deep learning (DL) and convolutional neural network (CNN) approaches are used, and methods to detect train driver Drowsiness using YOLOv7 and YOLOv8 models are presented. YOLOv7 and YOLOv8 are the most recent object detection models that are effective for various tasks, including driver Drowsiness detection. Our studies show that YOLOv8 outperforms YOLOv7 in terms of accuracy, speed of processing and learning, and required memory in detecting train driver Drowsiness. Real-time detection features using YOLOv8 models are also demonstrated. These features can be used to detect Drowsiness in real time, which can help prevent accidents.Safety is one of the most important priorities of the rail transport industry. Train driver sleepiness is a major threat to railway safety as it can lead to accidents and irreparable losses. In recent years, artificial intelligence and machine learning have emerged as promising approaches for developing driver Drowsiness detection strategies. In this article, both deep learning (DL) and convolutional neural network (CNN) approaches are used, and methods to detect train driver Drowsiness using YOLOv7 and YOLOv8 models are presented. YOLOv7 and YOLOv8 are the most recent object detection models that are effective for various tasks, including driver Drowsiness detection. Our studies show that YOLOv8 outperforms YOLOv7 in terms of accuracy, speed of processing and learning, and required memory in detecting train driver Drowsiness. Real-time detection features using YOLOv8 models are also demonstrated. These features can be used to detect Drowsiness in real time, which can help prevent accidents.

The main objective of this project is to detect driver’s Drowsiness and alert the driver which is an important precautionary measure in order to avoid accidents. Here two different algorithms based on Convolution Neural Network (CNN) were applied and the results were compared respectively. “Highway Hypnosis” is a serious issue to be addressed while driving especially on highways. Drivers who travel on highways continuously for more than 3 hours must be aware of this serious problem. If there is proper knowledge of it, fatalities would be drastically reduced. In this project, a dedicated detection coupled with an alarm system is provided to alert the driver in case of Drowsiness. CNN is used since it is very effective in analyzing images and videos. In this project, a live video feed is used to detect Drowsiness by suitable algorithms.

Today most accidents are caused by drivers’ fatigue, Drowsiness and losing attention on the road ahead. In this paper, a system is introduced, using RGB-D cameras to automatically identify Drowsiness and give warning. In this system two important modules have been utilized simultaneously to identify the state of driver’ s mouth and eyes for detecting Drowsiness. At first, using the depth information, the mouth area and its state are identified. Then using CNN networks, to predict whether the eyes are open or closed, a semi-VGG architecture is used. The results of yawning and eyes states detection are integrated to decide whether an alarm should be issued. The results show an accuracy of about 90% which is encouraging.

Background: Drowsiness is one of the underlying causes of driving accidents, which contribute, to many road fatalities annually. Although numerous methods have been developed to detect the level of Drowsiness, tech-niques based on image processing are quicker and more accurate in comparison with the other methods. The aim of this study was to use image-processing techniques to detect the levels of Drowsiness in a driving simula-tor. Methods: This study was conducted on five suburban drivers using a driving simulator based on virtual reality laboratory of Khaje-Nasir Toosi University of Technology in 2015 Tehran, Iran. The facial expressions, as well as location of the eyes, were detected by Violla-Jones algorithm. Criteria for detecting drivers’ levels of drowsi-ness by eyes tracking included eye blink duration blink frequency and PERCLOS that was used to confirm the results. Results: Eye closure duration and blink frequency have a direct ratio of drivers’ levels of Drowsiness. The mean of squares of errors for data trained by the network and data into the network for testing, were 0. 0623 and 0. 0700, respectively. Meanwhile, the percentage of accuracy of detecting system was 93. Conclusion: The results showed several dynamic changes of the eyes during the periods of Drowsiness. The present study proposes a fast and accurate method for detecting the levels of drivers’ Drowsiness by considering the dynamic changes of the eyes.

Purpose: Drowsy driving accounts for many accidents and has attracted substantial research attention in recent years. Electroencephalography (EEG) signals are shown to be a reliable measure for the early detection of Drowsiness. Unfortunately, there is no comprehensive study showing the applicability of Drowsiness detection systems with EEG signals. In this research, we targeted the studies under the category of Drowsiness detection, which adopted an EEG-based approach, to inspect the applicability of these systems from different aspects. Materials and Methods: We included documented studies that utilized clinical devices and consumer-grade EEG headsets for detection of Drowsiness and investigated the selected studies from different aspects such as the number of EEG channels, sampling frequency, extracted features, type of classifiers, and accuracy of detection. Among available headsets, we focused on the most popular ones, namely Muse, NeuroSky, and EMOTIV brands. Results: Considerable number of studies have used EEG headsets, and their reports showed that the highest average accuracy belongs to EMOTIV, and the highest maximum detection accuracy, 98. 8%, was achieved by the Muse headset. Spectral features extracted from short periods of 1, 2, or 10 secs are the most popular features, and the support vector machine is the most commonly used classifier in Drowsiness detection systems. Therefore, implementing a reliable detection system does not necessarily include complicated features and classifiers. Conclusion: It is shown that, despite their few electrodes, commercial headsets have gained decent detection accuracy. This study sheds light on the current status of Drowsiness detection systems and paves the way for future industrial designs of such systems.

Background and Objective: Driver Drowsiness is a cause of many traffic accidents all around the world. Driver respiration dynamics undergoes significant changes from wakefulness to sleep. The intrusive nature of current respira-tion monitoring methods makes them unattractive for detection of in-vehicle driver Drowsiness. In this paper, changes in the respiration rate were monitored for Drowsiness detection using thermal imaging, which is completely contact-free and non-intrusive. Materials and Methods: For each frame, the driver’ s face was isolated from the rest of the image. Then, the nostrils' zone was localized using physiological characteristics of face. The respiration signal was constructed by putting together the mean temperature of nostril region in all of the frames. In order to study respiration variations from wakefulness to Drowsiness, a total number of 12 subjects were tested in a driving simulator. The observer rating of Drowsiness (ORD) method was used to estimate the Drowsiness level of the subjects. Results: Derivation of driver respiration rate using thermal imaging was a reliable and non-intrusive method. The results were strongly correlated with those of traditional methods. Driver respiration rate decreased from wakefulness to extreme Drowsiness by 3. 5 breaths per minute (bpm) and its standard deviation (SD) increased by 0. 7 bpm. Conclusion: Driver respiration rate decreased and its SD increased from wakefulness to Drowsiness in 12 participants of this study. The results showed that driver Drowsiness could be detected even at moderate levels from analysis of the respiration rate.