A CNN-Based Driver’s Drowsiness and Distraction Detection System

SOMN_IA: Portable and Universal Device for Real-Time Detection of Driver’s Drowsiness and Distraction Levels

“…To localize the face region, we used the MediaPipe face detector [16] because it is designed for mobile implementation, providing an accurate localization with low computational complexity [12]. We compared the MediaPipe face detector with a Haar-based face detector [15] from computer complexity and accuracy points of view.…”

“…We compared the MediaPipe face detector with a Haar-based face detector [15] from computer complexity and accuracy points of view. The comparison results show that the MediaPipe face detector provides a higher detection accuracy with fewer false positive errors and slightly faster detection speed [12]. In the MediaPipe face detector, six reference points (Figure 4) are used for a guideline to generate a bounding box.…”

“…Although this approach provides better performance, it is invasive to the driver because the driver must use the wearable sensors during driving. Finally, in the visual-based approach, the driver's face image or video data are analyzed to determine the driver's drowsiness and distraction levels [11][12][13][14]. The principal advantage of this approach is that it is not invasive for drivers and the performance does not depend on the driver's driving skill, vehicle type, or the road conditions.…”

“…For example, the face localization is carried out using a Haar-based face detector [15] or a MediaPipe face detector [16]. The drowsiness detection and/or distraction are carried out using some convolutional neural networks (CNN) [13,14], such as VGG16, MobileNet, ResNet, and some specifically designed CNNs for this purpose [11,12]. Almost all systems using this approach perform well in a laboratory environment using the GPU-based workstation, in which there are not any limitations in computing power, memory space, or energy consumption.…”

“…The proposed device uses a new generation of techniques employing a graphical interface as well as passive and active alarms, which allows one to communicate to the driver if he or she is driving dangerously, to avoid a possible accident. Unlike previously proposed devices in different research papers [11][12][13][14], SOMN_IA is a universal system, which allows its implementation in any type of vehicle because the device can be connected using a plug-in BUCK-type DC-DC converter to the car battery. The SOMN_IA contains, besides a main operation unit, an input-port for a single camera with a visible and an infrared spectrum, and a graphical interface, including a sound alarm to inform the driver about his/her dangerous state.…”