Fatigue Driving Detection Based on Improved YOLOV5

Abstract

Fatigue driving detection based on YOLOV5 object detection algorithm. YOLOV5N with fewer parameters is selected as the basic model, and the large object detection layer in YOLOV5N is removed according to the object size clustering results, which reduces the parameters and improves the detection results. SAM is introduced to improve the ability of the backbone network to extract key features, and the convolution kernel in SAM is expanded to provide a wider receptive field for the model, in exchange for better detection results with a small increase in parameters. Referring to BiFPN, the Neck part of YOLOV5N is modified to provide more diverse fusion methods for multi-scale features. The precision, recall and mAP of the improved model are higher than those of YOLOV5N.