Lightweight CNN-Based Driver Eye Status Surveillance for Smart Vehicles

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 article proposes a driver eye status surveillance system based on lightweight convolutional neural networks (CNNs). The overall system consists of the following three stages: Face detection, eye detection, and eye classification. In the first stage, the system utilizes a small real-time face detector, named nano YOLO5Face. The second stage focuses on exploiting the compact CNN network architecture combined with the inception network, and 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 33.12 frames per second (FPS) and 25.11 FPS on an Intel Core I7-4770 CPU @ 3.40 GHz [personal computer (PC)] and a 128-core Nvidia Maxwell GPU (Jetson Nano device), respectively.