Detection of Driver Drowsiness and Distraction Using Computer Vision and Machine Learning Approaches

Drowsiness and distracted driving are leading factor in most car crashes and near-crashes. This research study explores and investigates the applications of both conventional computer vision and deep learning approaches for the detection of drowsiness and distraction in drivers. In the first part of this MPhil research study conventional computer vision approaches was studied to develop a robust drowsiness and distraction system based on yawning detection, head pose detection and eye blinking detection. These algorithms were implemented by using existing human crafted features. Experiments were performed for the detection and classification with small image datasets to evaluate and measure the performance of system. It was observed that the use of human crafted features together with a robust classifier such as SVM gives better performance in comparison to previous approaches. Though, the results were satisfactorily, there are many drawbacks and challenges associated with conventional computer vision approaches, such as definition and extraction of human crafted features, thus making these conventional algorithms to be subjective in nature and less adaptive in practice. In contrast, deep learning approaches automates the feature selection process and can be trained to learn the most discriminative features without any input from human. In the second half of this research study, the use of deep learning approaches for the detection of distracted driving was investigated. It was observed that one of the advantages of the applied methodology and technique for distraction detection includes and illustrates the contribution of CNN enhancement to a better pattern recognition accuracy and its ability to learn features from various regions of a human body simultaneously. The comparison of the performance of four convolutional deep net architectures (AlexNet, ResNet, MobileNet and NASNet) was carried out, investigated triplet training and explored the impact of combining a support vector classifier (SVC) with a trained deep net. The images used in our experiments with the deep nets are from the State Farm Distracted Driver Detection dataset hosted on Kaggle, each of which captures the entire body of a driver. The best results were obtained with the NASNet trained using triplet loss and combined with an SVC. It was observed that one of the advantages of deep learning approaches are their ability to learn discriminative features from various regions of a human body simultaneously. The ability has enabled deep learning approaches to reach accuracy at human level.

A CNN-LSTM-based Deep Learning Approach for Driver Drowsiness Prediction

Abstract: The development of neural networks and machine learning techniques has recently been the cornerstone for many applications of artificial intelligence. These applications are now found in practically all aspects of our daily life. Predicting drowsiness is one of the most particularly valuable of artificial intelligence for reducing the rate of traffic accidents. According to earlier studies, drowsy driving is at responsible for 25 to 50% of all traffic accidents, which account for 1,200 deaths and 76,000 injuries annually. The goal of this research is to diminish car accidents caused by drowsy drivers. This research tests a number of popular deep learning-based models and presents a novel deep learning-based model for predicting driver drowsiness using a combination of convolutional neural networks (CNN) and Long-Short-Term Memory (LSTM) to achieve results that are superior to those of state-of-the-art methods. Utilizing convolutional layers, CNN has excellent feature extraction abilities, whereas LSTM can learn sequential dependencies. The National Tsing Hua University (NTHU) driver drowsiness dataset is used to test the model and compare it to several other current models as well as state-of-the-art models. The proposed model outperformed state-of-the-art models, with results up to 98.30% for training accuracy and 97.31% for validation accuracy

A CNN-LSTM-based Deep Learning Approach for Driver Drowsiness Prediction

Abstract: The development of neural networks and machine learning techniques has recently been the cornerstone for many applications of artificial intelligence. These applications are now found in practically all aspects of our daily life. Predicting drowsiness is one of the most particularly valuable of artificial intelligence for reducing the rate of traffic accidents. According to earlier studies, drowsy driving is at responsible for 25 to 50% of all traffic accidents, which account for 1,200 deaths and 76,000 injuries annually. The goal of this research is to diminish car accidents caused by drowsy drivers. This research tests a number of popular deep learning-based models and presents a novel deep learning-based model for predicting driver drowsiness using a combination of convolutional neural networks (CNN) and Long-Short-Term Memory (LSTM) to achieve results that are superior to those of state-of-the-art methods. Utilizing convolutional layers, CNN has excellent feature extraction abilities, whereas LSTM can learn sequential dependencies. The National Tsing Hua University (NTHU) driver drowsiness dataset is used to test the model and compare it to several other current models as well as state-of-the-art models. The proposed model outperformed state-of-the-art models, with results up to 98.30% for training accuracy and 97.31% for validation accuracy

Automatic Driver Drowsiness Detection System

The proposed system aims to lessen the number of accidents that occur due to drivers’ drowsiness and fatigue, which will in turn increase transportation safety. This has become a common reason for accidents in recent times. Several facial and body gestures are considered signs of drowsiness and fatigue in drivers, including tiredness in the eyes and yawning. These features are an indication that the driver’s condition is improper. EAR (Eye Aspect Ratio) computes the ratio of distances between the horizontal and vertical eye landmarks, which is required for the detection of drowsiness. For the purpose of yawn detection, a YAWN value is calculated using the distance between the lower lip and the upper lip, and the distance will be compared against a threshold value. We have deployed an eSpeak module (text-to-speech synthesiser), which is used for giving appropriate voice alerts when the driver is feeling drowsy or is yawning. The proposed system is designed to decrease the rate of accidents and contribute to technology with the goal of preventing fatalities caused by road accidents. Over the past ten years, advances in artificial intelligence and computing technologies have improved driver monitoring systems. Several experimental studies have gathered data on actual driver fatigue using different artificial intelligence systems. In order to dramatically improve these systems' real-time performance, feature combinations are used. An updated evaluation of the driver sleepiness detection technologies put in place during the previous ten years is presented in this research. The paper discusses and displays current systems that track and identify drowsiness using various metrics. Based on the information used, each system can be categorised into one of four groups. Each system in this paper comes with a thorough discussion of the features, classification rules, and datasets it employs.&nbsp

Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50

Traffic accidents are fatal events that need special attention. According to research by the National Transportation Safety Committee, 80% of traffic accidents are caused by human error, one of which is tired and drowsy drivers. The brain can interpret the vital fatigue of a drowsy driver sign as yawning. Therefore, yawning detection for preventing drowsy drivers’ imprudent can be developed using computer vision. This method is easy to implement and does not affect the driver when handling a vehicle. The research aimed to detect drowsy drivers based on facial expression changes of yawning by combining the Haar Cascade classifier and a modified pre-trained model, MobileNetV2 and ResNet50. Both proposed models accurately detected real-time images using a camera. The analysis showed that the yawning detection model based on the ResNet50 algorithm is more reliable, with the model obtaining 99% of accuracy. Furthermore, ResNet50 demonstrated reproducible outcomes for yawning detection, considering having good training capabilities and overall evaluation results