Research on Vehicle Detection Algorithm in Road Scene Based on Optimized YOLOv5s
In order to solve the problem of time-consuming and labor-intensive manual identification of vehicles,the research on intelligent vehicle detection algorithm for road scenes is carried out to achieve intelligent vehicle detection.A road vehicle detection model based on deep learning is proposed.By using the lightweight and easy to deploy and develop YOLOv5s model as the basic model,three classic attention modules,CA,SE and CBAM,are introduced to replace the C3 module in the backbone network of YOLOv5,so that the network model can better focus on the vehicle area and improve the accuracy of vehicle detection.This enables the model to better adapt to vehicle detection requirements in complex road scenarios while maintaining efficiency and ease of use.The experimental results show that after the CBAM attention module is introduced into the network model,the mean average precision(mAP)of vehicle detection on the UA-DETRAC dataset can reach 92.3%,which is better than other attention modules.The results of this research are of great significance for the realization of intelligent vehicle detection,and are expected to provide more reliable solutions for applications such as road traffic monitoring and driving assistance systems.
万方数据
维普
