Research and Application on Cone Bucket Identification Technology Based on YOLOv5
Based on computer vision theory and object detection algorithm,the cone buckets was realized by using YOLOv5 model and homemade datasets.Then the trained weight was deployed to the ROS smart car to realize the autonomous obstacle avoidance function in the car autonomous driving.Experimental data showed that this paper achieved 97.36%mAP@0.5 by using only 95 pictures and 514 markers after 50 epochs of training,which is very good in the recognition of cone buckets,and has strong generalization ability.This cone buckets recognition model effectively improves the recognition accuracy in complex optical scene and dense cone buckets targe t.
NETL
NSTL
万方数据
维普
