Jul 2026· International Scientific Journal of Engineering and Management· Vol 05, pp. 1-9· 0 citations
TL;DR
This paper introduces a non-intrusive, real-time Driver Drowsiness Detection and Alert System utilizing computer vision and lightweight deep learning models, providing an affordable, robust solution to enhance smart vehicular safety.
Abstract
ABSTRACT:
Driver drowsiness is one of the primary catalysts for highway accidents globally, resulting in significant loss of life and financial damage. Traditional fatigue detection systems often rely on invasive sensors or coarse vehicular telemetry that fail to capture sudden micro-sleep events. This paper introduces a non-intrusive, real-time Driver Drowsiness Detection and Alert System utilizing computer vision and lightweight deep learning models. The proposed system captures live video streams, extracts 68 facial landmark coordinates using dlib's specialized shape predictor, and calculates temporal changes in the Eye Aspect Ratio (EAR) to determine eye-closure rates. To handle varied environmental conditions and visual occlusions, a Convolutional Neural Network (CNN) is integrated to analyze spatial eye states. Upon identifying sustained eye closure exceeding a safe threshold, the system triggers localized audio alarms and dispatches emergency SMS alerts containing real-time GPS metadata via the Twilio API. Operating seamlessly on lightweight client environments, this system balances high predictive accuracy (96.4%) with low latency, providing an affordable, robust solution to enhance smart vehicular safety.
Keywords:
Driver Drowsiness Detection, Computer Vision, Deep Learning, Convolutional Neural Networks (CNN), Eye Aspect Ratio (EAR), dlib, OpenCV, Real-time Alert System, Smart Vehicle Safety, Twilio API.
Road traffic accidents caused by driver fatigue continue to be a major public safety concern, highlighting the need for intelligent and real-time monitoring systems. This paper presents a Smart Driver Drowsiness Detection and Advanced Emergency Communication System that combines computer vision, deep learning, and automated emergency response to enhance driver safety. The proposed system employs MediaPipe Face Mesh to accurately detect facial landmarks and localize the driver’s eye regions from live video captured through a standard webcam. A Convolutional Neural Network (CNN) is utilized to classify the eye state as open or closed, while an adaptive eyescore mechanism continuously evaluates the driver’s alertness by analyzing consecutive eye-closure patterns. When signs of prolonged drowsiness are detected, the system immediately activates an audible warning to regain the driver’s attention. If the driver fails to respond after multiple warning cycles, the system initiates an automated emergency communication process by obtaining the current GPS location, generating a Google Maps navigation link, transmitting an emergency email to predefined contacts, and placing an emergency phone call using Android Debug Bridge (ADB). Unlike conventional driver monitoring systems that focus solely on fatigue detection, the proposed framework integrates an intelligent emergency response mechanism to provide timely assistance during critical situations. The system is entirely nonintrusive, operates in real time using inexpensive hardware, and does not require wearable sensors, making it practical for deployment in modern vehicles. Experimental evaluation demonstrates that the integration of MediaPipe and CNN enables reliable eyestate recognition with low computational complexity, providing an effective solution for reducing fatigue-related accidents and improving road safety.
Dr M Venkata, Yamuna Chirumalla· International Journal of Eng...· 0 citations
Driver drowsiness is among the main causes of road accidents worldwide and can lead to dreadful injuries. Early fatigue detection significantly helps to avoid crashes and enhance road safety. Conventional driver monitoring devices are typically designed to monitor behavior, such as eye blinking and yawning, or physiological indicators, such as EEG and heart rate. Moreover, single-modal systems usually experience lower accuracy because of environmental fluctuations, including lighting changes, occlusions and variations in camera angles. In this study, a hybrid CNN-EAR-based real-time driver drowsiness detection system that combines machine learning-based analyses of faces with geometrical monitoring using eye movement is presented to establish the reliability of the detection. The network utilized is that of a convolutional neural network (CNN) that identifies and classifies areas of faces and eyes under different environmental conditions. Moreover, the eye aspect ratio (EAR) method is a continuous method for assessing eye openness to identify the frequency of blink and extended eye closure. Information at the decision level of a fusion mechanism is used to combine CNN prediction and EAR threshold values to minimize false positives and enhance detection strength. Through experimental analysis, one can prove that the hybrid system is more accurate, shows better performance in terms of precision–recall, and exhibits a faster inference speed than single CNN or EAR-based methods do. The suggested framework is simple and affordable and can be incorporated into real-time intelligent driver assistance systems (IDASs). The solutions that will be implemented in the future include night vision assistance by using infrared cameras, estimation of head pose, and multimodal fatigue detection to make it more dependable.
S. Venkatesh, M. Chithra· 2026 4th International Confe...· 0 citations
Road accidents caused by driver drowsiness and distraction are a major global concern, as fatigue and inattention significantly slow reaction time and increase accident risk. To address this issue, this paper proposes a vision-based driver monitoring system using facial analysis from an on-board camera. The system detects the face and extracts key regions of interest, including the eyes, mouth, and head, which are analyzed independently using convolutional neural networks. Temporal information is captured by aggregating the convolutional neural network (CNN) outputs over a fixed time window. Drowsiness is estimated by fusing eye and mouth features with a multilayer perceptron, while distraction is detected based on head movements. An important advantage of the proposed approach is its robustness to partial input loss, allowing the system to remain functional even when some facial regions are missing or occluded, such as when wearing sunglasses or face masks. Experimental results show high detection accuracy, reaching 97.3% for drowsiness and 98% for distraction under ideal conditions, with only limited performance degradation in challenging scenarios. These results confirm the suitability of the proposed system for real-time driver monitoring applications.
Sara Benkouider, N. Lagraa· International Journal of Ele...· 0 citations
Driver drowsiness remains one of the leading causes of road traffic accidents worldwide, as fatigue significantly impairs a driver's alertness, reaction time, and decision-making ability. Existing drowsiness detection approaches often require specialized hardware or computationally intensive models that limit their deployment on resource-constrained mobile devices. This study presents a lightweight CNN-based Android vision system for real-time driver drowsiness detection using eye-state analysis. The proposed system combines Haar Cascade classifiers for real-time face and eye localization with a custom Convolutional Neural Network (CNN) for classifying eye states as open or closed. A score-based decision mechanism continuously monitors eye closure across successive video frames and activates an audible alarm when prolonged eye closure indicates possible driver fatigue. The CNN model was trained using labeled eye-image datasets in Python with the Keras deep learning framework and subsequently integrated into an Android application for real-time inference using a smartphone camera. Experimental evaluation demonstrated that the developed system accurately classified eye states and generated timely drowsiness alerts under real-time operating conditions while maintaining computational efficiency suitable for mobile deployment. The findings demonstrate that lightweight deep learning models can be effectively deployed on Android devices to provide an accessible, low-cost, and practical solution for continuous driver drowsiness monitoring and improved road safety.
Benisemeni Esther Zakka, Fabunmi Esther Omowunmi, Gloria Ngozi Jola et al.· International Journal of Nat...· 0 citations
Drowsiness is a leading cause of human error in transportation and in shift-based occupational work, yet delivering reliable real-time detection on affordable, resource-constrained hardware remains difficult. This study aims to develop and evaluate a vision-based drowsiness detection system that behaves consistently across a full-power desktop and a low-cost edge device. The system couples two independent MobileNetV2 transfer-learning classifiers — one for eye state (Open/Closed) and one for mouth state (Yawn/No_yawn) — with a temporal decision engine that converts frame-level predictions into microsleep and excessive-yawning alerts. Both classifiers were trained on a merged multi-source dataset (8,548 training images) and evaluated with a class-balanced protocol (186 images/class for the eye branch and 448 images/class for the mouth branch) to remove test-set imbalance bias. The decision engine was realised as two platform-appropriate pipelines that share an offline-first, retry-capable event architecture: a duration-based, two-tier hysteresis alert on a desktop application (Haar-cascade detection, H5/float32 models) and a frame-count alert designed for a Raspberry Pi 5 edge board (MediaPipe detection, quantised TensorFlow Lite models). On the class-balanced test set the eye branch reached 95.16% (H5) / 95.97% (TFLite) accuracy and the mouth branch reached 96.65% for both formats, with above-99% cross-format prediction agreement. Converting to TFLite cut model size by 73.4% (8.99 to 2.39 MB) and single-frame model inference latency roughly thirteen-fold (about 25 to 1.9 ms, measured on the development machine). Real-time desktop sessions sustained 6.9–12.0 FPS, and the Haar detector located a face in only 15.8% of off-angle frames versus 96.9–98.6% of frontal frames. This single-session result offers a preliminary, rather than definitive, indication of the detector's pose sensitivity. A lightweight dual-MobileNetV2 design with platform-appropriate detectors shows promise for delivering consistent real-time drowsiness alerts across heterogeneous hardware tiers.
Rafi'e· Indonesian Journal of Electr...· 0 citations
Road accidents caused by unexpected animal crossings are a major concern, especially during nighttime when visibility is poor. To address this issue, the proposed system introduces an advanced animal detection and alert framework designed to enhance road safety through continuous monitoring. The system employs a high-resolution night-vision camera to capture real-time footage of roadways. Deep learning models such as Convolutional Neural Networks (CNN) and YOLO are used to accurately identify animals even under low-light or foggy conditions. Once an animal is detected, the system immediately triggers alert signals to warn approaching vehicles, thereby reducing the chances of collision. This intelligent approach minimizes the need for human intervention and provides a scalable solution for highways and rural roads. The integration of AI-based vision technology with real-time detection ensures efficient performance and faster response. By combining automation, deep learning, and alert mechanisms, the proposed system aims to improve nighttime driving safety and prevent animal-related road accidents.
G. S, R. S, Sanjay A. K. et al.· 2026 4th International Confe...· 0 citations
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