YOLO-Based Deep Learning Design for In-Cabin Monitoring System with Fisheye-Lens Camera

Abstract

To exploit an image-based in-cabin monitoring system for driving behavior and in-vehicle occupants detections to improve driving safety, in this paper, by installing a fisheye-lens camera at the in-car roof center and by using RGB-format images as inputs, the YOLO-based deep learning models, including YOLOv3-tiny, YOLOv3-tiny-3I, YOLO-fastest, YOLO-fastest-xl, and YOLO-fastest-three scales, are studied to be candidate detectors. The proposed in-cabin monitoring design can detect the normal and distracted driving cases and in-vehicle occupants including back seat passengers and pet dogs. The experimental results show that the YOLO-fastest-three scales model performs the best metrics for F1-Score and mAP, which are 95.89% and 97.16%, respectively. The YOLO-fastest-xl model has the best metric for false negative rate (FNR), which is 2.63%. By the software realization, the proposed design executes up to 30 frames per second (FPS) with the GPU-based embedded device.