{'fraction_non_alphanumeric': 0.02697841726618705, 'fraction_numerical': 0.008992805755395683, 'mean_word_length': 4.888888888888889, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Novel Real-time Driver Monitoring System Based on Deep Convolutional Neural Network

In this paper, we propose a novel real-time driver monitoring system based on deep convolutional neural network. Our system can efficiently detect head and facial features. It also allows to accurately estimate the distance between driver's head and camera, and vertical and horizontal rotation angles of head. Our work is inspired by the third version of YOLO (YOLOv3), a well-known objects detection algorithm. We have successfully introduced important improvements on YOLOv3 to further fasten the detection speed for excellent accuracy. Indeed, we reduced the depth and width of the backbone network of YOLO. We called this refined network HeadNet. Then, we implemented K-means algorithm to find appropriate anchors for head and facial features. Furthermore, we designed a new four-layer network knows as OrganNet to detect facial features. Our experiments show that our system has a high detection accuracy of 96.2% to detect head, and 91.5% to detect facial features, with a fast detection speed of 18 frames per second.