Detection of Drowsiness among Drivers Using Novel Deep Convolutional Neural Network Model

Abstract

Detecting drowsiness among drivers is critical for ensuring road safety and preventing accidents caused by drowsy or fatigued driving. Research on yawn detection among drivers has great significance in improving traffic safety. Although various studies have taken place where deep learning-based approaches are being proposed, there is still room for improvement to develop better and more accurate drowsiness detection systems using behavioral features such as mouth and eye movement. This study proposes a deep neural network architecture for drowsiness detection employing a convolutional neural network (CNN) for driver drowsiness detection. Experiments involve using the DLIB library to locate key facial points to calculate the mouth aspect ratio (MAR). To compensate for the small dataset, data augmentation is performed for the ‘yawning’ and ‘no_yawning’ classes. Models are trained and tested involving the original and augmented dataset to analyze the impact on model performance. Experimental results demonstrate that the proposed CNN model achieves an average accuracy of 96.69%. Performance comparison with existing state-of-the-art approaches shows better performance of the proposed model.

Enhancing Road Safety: Deep Learning-Based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems

“…What is significant is the continuous enhancement in parameters when using the DLID3-ADAS technique, which is aimed at minimizing the discrepancies between predictions and actual TR labels. To confirm the improved performance of the DLID3-ADAS method, a brief comparison with other methods was conducted, and the results are shown in Table 4 and Figure 12 [22]. The comparative findings demonstrate that the DLID3-ADAS technique achieves exceptional performance over other models.…”

“…In terms of 𝑎𝑐𝑐𝑢 𝑦 , the DLID3-ADAS technique achieves an increased 𝑎𝑐𝑐𝑢 𝑦 of 97.05% while the YOLOv3-tiny CNN, SVM, LSTM NN, Dlib + linear SVM, 2s-STGCN, Dlib + 15-layer CNN, and 3D Deep CNN models attain smaller 𝑎𝑐𝑐𝑢 𝑦 values of 94.32%, 89.00%, 88.00%, 92.50%, 93.40%, 96.69%, and 96.80%, respectively. To confirm the improved performance of the DLID3-ADAS method, a brief comparison with other methods was conducted, and the results are shown in Table 4 and Figure 12 [22]. The comparative findings demonstrate that the DLID3-ADAS technique achieves exceptional performance over other models.…”

“…Chen et al [21] developed a multi-modal fusion-based FER system that is efficient in precisely identifying facial expressions irrespective of lighting cases and head positions, utilizing a structured-light imaging camera that gives three modalities of images-Depth Maps, near-infrared (NIR), and RGB. Majeed et al [22] designed a deep neural network (DNN) framework for driver drowsiness identification by exploiting an CNN.…”

A Real-Time Automated Defect Detection System for Ceramic Pieces Manufacturing Process Based on Computer Vision with Deep Learning

“…Data augmentation is an important technique when working with small datasets to enhance outcomes [ 17 ]. It helps prevent overfitting and underfitting problems during training by expanding the range of datasets and helping the model identify important patterns.…”

“…By augmenting the available data, the model can enhance its robustness, adaptability, and ability to achieve superior generalization performance [ 18 ]. This methodology facilitates the artificial generation of new images in an efficient and convenient manner, using techniques such as perspective skewing, elastic distortions, rotation, shearing, cropping, and mirroring [ 17 ]. However, for our specific case, we decided to limit the augmentation process to rotation and flipping.…”