Application of Improved YOLO Algorithm for Pedestrian Detection and Slow Traffoc Planning
Pedestrians are a crucial category of targets in object detection applications,and the real-time and accurate counting of pedestrian flow has the significant practical significance in various fields.By setting up a deep learning environment,establishing the pedestrian datasets,and re-annotating,training and testing the pedestrians,the accuracy,missed detection rate and false detection rate of pedestrian targets in videos are statistically analyzed.The results indicate that the pedestrian detection model based on improved YOLO v4 can more accurately and efficiently identify the pedestrian targets in dense scenes,and the expected goals are achieved so as to provide the application value for planning of urban slow-traffic system and commercial districts.
NETL
NSTL
万方数据
