A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification
Source abstract
Abstract
Drowsiness detection is an important task in road safety and other areas that require sustained attention. In this article, an approach to detect drowsiness in drivers is presented, focusing on the eye region, since eye fatigue is one of the first symptoms of drowsiness. The method used for the extraction of the eye region is Mediapipe, chosen for its high accuracy and robustness. Three neural networks were analyzed based on InceptionV3, VGG16 and ResNet50V2, which implement deep learning. The database used is NITYMED, which contains videos of drivers with different levels of drowsiness. The three networks were evaluated in terms of accuracy, precision and recall in detecting drowsiness in the eye region. The results of the study show that all three convolutional neural networks have high accuracy in detecting drowsiness in the eye region. In particular, the Resnet50V2 network achieved the highest accuracy, with a rate of 99.71% on average. For better visualization of the data, the Grad-CAM technique is used, with which we obtain a better understanding of the performance of the algorithms in the classification process.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sleep and Work-Related Fatigue · Air Quality Monitoring and Forecasting · Non-Invasive Vital Sign Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
LIECAR Laboratory, University of San Antonio Abad del Cusco (UNSAAC), Cuzco 08000, Peru
PAVIC Laboratory, University of Acre (UFAC), Rio Branco 69915-900, Brazil
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