现代信息科技2024，Vol.8Issue(1)：121-124,129,5.DOI:10.19850/j.cnki.2096-4706.2024.01.025
基于深度学习的行人和车辆检测与跟踪研究
Research on Pedestrian and Vehicle Detection and Tracking Based on Deep Learning
袁旻颉 1罗荣芳 1陈静 1苏成悦2
作者信息
- 1. 广东工业大学 物理与光电工程学院,广东 广州 510006
- 2. 广东工业大学 物理与光电工程学院,广东 广州 510006||广东工业大学 先进制造学院,广东 揭阳 515548
- 折叠
摘要
Abstract
This paper proposes a multi-objective detection and tracking algorithm combining improved YOLOv5 and improved Deep SORT to address the issues of insufficient detection accuracy,lost tracking targets,and identity switching in pedestrian and vehicle's multi-target detection and tracking.Replacing binary cross entropy loss function with Varifocal Loss in the detection phase,combined with CA attention mechanism and DIoU_NMS algorithm.During the tracking phase,replace the feature extraction network of the REID module of Deep SORT with EfficientNetV2-S.In COCO dataset detection,map@0.5 reaches 78%,an improvement of 4.5%compared to the original model.On the MOT16 dataset tracking,the MOTA reaches 58.1,an improvement of 5.7 compared to the original model.The IDswitch is reduced by 516 times,which is equivalent to a reduction of 55.1%.The test results show that the algorithm has good practical application value.关键词
深度学习/目标检测/目标跟踪/计算机视觉Key words
Deep Learning/object detection/object tracking/computer vision分类
信息技术与安全科学引用本文复制引用
袁旻颉,罗荣芳,陈静,苏成悦..基于深度学习的行人和车辆检测与跟踪研究[J].现代信息科技,2024,8(1):121-124,129,5.
