Li Yuqiong
School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410000, Hunan, ChinaZhou Yongjun
School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410000, Hunan, ChinaJiang Shuxia
School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410000, Hunan, ChinaLiang Yang
School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410000, Hunan, ChinaSchool of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410000, Hunan, China
TP389.1
Aiming at the problem of low accuracy of small target detection in traffic sign recognition tasks,which caused by that most of traffic signs in actual scene are small and dense,this paper proposes an improved YOLOv5 algorithm.firstly,embedding the CBAM into the Backbone and Neck of YOLOv5 network to improve the network feature extraction ability.and in order to solve the problem of slow network converge caused by GIOU Loss, DIoU Loss was used as the regression Loss function of the network. Experimental results show that the improved algorithm reaches 96.40% mAP in traffic sign recognition task,which is 6.83% higher than the original YOLOv5 algorithm. Finally, sending the improved network into TX2 embedded system to recognize traffic signs in real video,the result shows that the improved algorithm can run smoothly in embedded system.
