计算机与数字工程2023，Vol.51Issue(11)：2546-2552,2579,8.DOI:10.3969/j.issn.1672-9722.2023.11.014
改进YOLOv5s的车辆目标检测算法研究与实现
Research and Implementation of Vehicle Target Detection Algorithm Based on Improved YOLOv5s
摘要
Abstract
Aiming at the requirements of the vehicle target detection algorithm in the actual traffic scene,such as occupying small resources,ensuring real-time performance and high accuracy,a vehicle target detection algorithm based on the improved YO-LOv5s is proposed.Firstly,GhostNet is introduced to improve the Backbone of YOLOv5s,which reduces the computation of the net-work and improves the detection speed.Secondly,the CBAM attention mechanism is integrated to improve the difficulty of accurate detection under various weather and light conditions.Then,Soft-NMS is used instead of NMS to reduce the problem of missing de-tection caused by traffic congestion.Finally,a comparative ablation experiment is conducted to verify the performance of the im-proved algorithm,and then it is deployed to the embedded device for testing.According to the experimental results,the resource oc-cupancy of the model is reduced by 34.76%under the condition that the improved algorithm guarantees high average accuracy,and the frame rate on the embedded platform can reach 29 frame/s,which can meet the requirements of practical applications.关键词
YOLOv5/目标检测/注意力机制/嵌入式平台/TensorRTKey words
YOLOv5/target detection/attention mechanism/embedded platform/TensorRT分类
信息技术与安全科学引用本文复制引用
周金治,景瑞琦,吴静,刘梦宇..改进YOLOv5s的车辆目标检测算法研究与实现[J].计算机与数字工程,2023,51(11):2546-2552,2579,8.基金项目
国家自然科学基金项目(编号:61771411)资助. （编号:61771411）
