Driver Eye Status Monitoring System Based on Lightweight Convolutional Neural Network Architectures for Low-computing Devices

Abstract

Traffic accidents are the leading death rate among accident categories. One of the major causes of road traffic accidents is driver drowsiness. Many studies have paid attention to this issue and developed driver assistance tools to reduce the risk. These methods mainly analyze driver behavior, vehicle behavior, and driver physiology. This research proposes a driver eye status monitoring system based on lightweight convolutional neural network (CNN) architectures. The overall system consists of three stages: face detection, eye detection, and eye classification. In the first stage, the system utilizes a small real-time face detector based on the YOLOv5 network, named YOLO5Face (YOLO5nFace). The second stage focuses on exploiting the compact CNN network architecture combined with the inception network, and Convolutional Triplet Attention mechanism. Finally, the system uses a simple classification network architecture to classify open or closed eye status. Additionally, this work also provides the datasets for the eye detection task comprised of 10,659 images and 21,318 labels. As a result, the real-time testing reached the best result at 33.12 FPS (frames per second) and 25.11 FPS on an Intel® CoreTM i7-4770 CPU @ 3.40GHz with 8 GB of RAM (Personal Computer - PC) and a 128-core Nvidia Maxwell GPU with 4 GB of RAM (Jetson Nano device), respectively. This speed is comparable with other previous techniques and it ensures that the proposed method can be applied in real-time systems for driver eye monitoring.