Application of Improved YOLOv5 Algorithm in Improving Vehicle Image Recognition Efficiency
The existing vehicle recognition algorithm models may not be accurate enough in feature extraction,and cannot effectively extract key features of vehicles.The generalization ability of the model is limited,and the recognition efficiency will be greatly reduced when facing new and unseen vehicles.Therefore,this article explores the application of improving the YOLOv5 algorithm in enhancing the efficiency of vehicle image recognition.By incorporating attention mechanisms and knowledge distillation techniques into the YOLOv5 algorithm,the model's ability to extract vehicle features and generalize has been effectively improved.The experimental results show that the key indicators of the improved algorithm have significantly improved,providing a more efficient and accurate solution for vehicle detection in intelligent transportation systems.
万方数据
维普
