A sleepy driver is arguably much more dangerous on the road than the one who is speeding as he is a victim of microsleeps. Automotive researchers and manufacturers are trying to curb this problem with several technological solutions that will avert such a crisis. This article focuses on the detection of such micro sleep and drowsiness using neural network-based methodologies. Our previous work in this field involved using machine learning with multi-layer perceptron to detect the same. In this paper, 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 achievement with this work is the capability to provide a lightweight alternative to heavier classification models with more than 88% for the category without glasses, more than 85% for the category night without glasses. On average, more than 83% of accuracy was achieved in all categories. Moreover, as for model size, complexity and storage, there is a marked reduction in the new proposed model in comparison to the benchmark model where the maximum size is 75 KB. 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.

Driver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application

A sleepy driver is arguably much more dangerous on the road than the one who is speeding as he is a victim of microsleeps. Automotive researchers and manufacturers are trying to curb this problem with several technological solutions that will avert such a crisis. This article focuses on the detection of such micro sleep and drowsiness using neural network based methodologies. Our previous work in this field involved using machine learning with multi-layer perceptron to detect the same. In this paper, 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 achievement with this work is the capability to provide a lightweight alternative to heavier classification models with more than 88% for the category without glasses, more than 85% for the category night without glasses. On average, more than 83% of accuracy was achieved in all categories. Moreover, as for model size, complexity and storage, there is a marked reduction in the new proposed model in comparison to the benchmark model where the maximum size is 75 KB. 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.

ISSN 2277-5099 | © 2016 Bonfring Abstract--In Indonesia, based on data from police, from the 2007-2010 at least 218 253 number of accidents occur. Approximately 65% of accidents occur due to human negligence. At the time of Eid 23 August to 7 September 2011, the number of accidents that occur most often caused by drowsy drivers (1,018 cases), followed by airworthiness vehicles (449 cases), roadworthiness (387 cases), and speed (155 cases). Drowsiness detection system created to reduce the risk of accident while driving. The system will records image of driver then face and eyes will be detected. Results of eyes detection, each frame value will be analyzed if eyes are closed for 4 seconds. If eyes close for 4 seconds then system will decide that driver is sleepy and alarm will sound. From the experiment, average result for detection is 954 ms, best position of camera is above the driver on the dashboard and for bright condition.

/pdf/driver-drowsiness-detection-system-4pruqa26gk.pdf

Driver Drowsiness Detection System

This paper focuses on the challenge of driver safety on the road and presents a novel system for driver drowsiness detection. In this system, to detect the falling sleep state of the driver as the sign of drowsiness, Convolutional Neural Networks (CNN) are used with regarding the two goals of real-time application, including high accuracy and fastness. Three networks introduced as a potential network for eye status classification in which one of them is a Fully Designed Neural Network (FD-NN) and others use Transfer Learning in VGG16 and VGG19 with extra designed layers (TL-VGG). Lack of an available and accurate eye dataset strongly feels in the area of eye closure detection. Therefore, a new comprehensive dataset proposed. The experimental results show the high accuracy and low computational complexity of the eye closure estimation and the ability of the proposed framework on drowsiness detection.

https://link.springer.com/content/pdf/10.1007/s42979-020-00306-9.pdf

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

Blink detection is an important technique in a variety of settings, including facial movement analysis and signal processing. However, automatic blink detection is very challenging because of the blink rate. This research work proposed a real-time method for detecting eye blinks in a video series. Automatic facial landmarks detectors are trained on a real-world dataset and demonstrate exceptional resilience to a wide range of environmental factors, including lighting conditions, face emotions, and head position. For each video frame, the proposed method calculates the facial landmark locations and extracts the vertical distance between the eyelids using the facial landmark positions. Our results show that the recognizable landmarks are sufficiently accurate to determine the degree of eye-opening and closing consistently. The proposed algorithm estimates the facial landmark positions, extracts a single scalar quantity by using Modified Eye Aspect Ratio (Modified EAR) and characterizing the eye closeness in each frame. Finally, blinks are detected by the Modified EAR threshold value and detecting eye blinks as a pattern of EAR values in a short temporal window. According to the results from a typical data set, it is seen that the suggested approach is more efficient than the state-of-the-art technique.

Adjusting eye aspect ratio for strong eye blink detection based on facial landmarks

Every year many people lose their lives due to fatal road accidents around the world and drowsy driving is one of the primary causes of road accidents and death. Fatigue and micro sleep at the driving controls are often the root cause of serious accidents. However, initial signs of fatigue can be detected before a critical situation arises and therefore, detection of driver’s fatigue and its indication is ongoing research topic. Most of the traditional methods to detect drowsiness are based on behavioural aspects while some are intrusive and may distract drivers, while some require expensive sensors. Therefore, in this paper, a light-weight, real time driver’s drowsiness detection system is developed and implemented on Android application. The system records the videos and detects driver’s face in every frame by employing image processing techniques. The system is capable of detecting facial landmarks, computes Eye Aspect Ratio (EAR) and Eye Closure Ratio (ECR) to detect driver’s drowsiness based on adaptive thresholding. Machine learning algorithms have been employed to test the efficacy of the proposed approach. Empirical results demonstrate that the proposed model is able to achieve accuracy of 84% using random forest classifier.

Real-Time Driver Drowsiness Detection System Using Eye Aspect Ratio and Eye Closure Ratio

Drivers drowsiness is one of the prime reasons for road accidents around the globe. Persistent monotonous driving for an extended period of time without rest leads to drowsiness and fatal mishaps. Automatic detection of driver's drowsiness can prevent a large number of road accidents and thus, can save valuable lives. In this work, an advanced system namely AD3S (Advanced Driver Drowsiness Detection System) using Android application has been developed. The system is capable of capturing real-time facial landmarks of the drivers. The facial landmarks are further utilized to compute several parameters namely Eye Aspect Ratio (EAR), Nose Length Ratio (NLR) and Mouth Opening Ratio (MOR) based on adaptive threshold which are capable of detecting driver's drowsiness. The highlighting feature of AD3S is that it is non-intrusive in nature and is cost effective. To test the efficacy of AD3S, machine learning and deep learning techniques have been applied over a data set of 1200 application users. The empirical results demonstrate that the proposed system is capable of detecting driver's drowsiness with an accuracy of approximately 98% with Bagging classifier.

Sukrit Mehta

Papers

Real-Time Driver Drowsiness Detection System Using Eye Aspect Ratio and Eye Closure Ratio

AD3S: Advanced Driver Drowsiness Detection System using Machine Learning