基于改进YOLOv8+DeepSORT的多目标车辆跟踪算法研究  

Research on Multi-target Vehicle Tracking Algorithm Based on Improved YOLOv8+DeepSORT

作　　者：徐英卓[1] 张渊博 刘晓天 XU Yingzhuo;ZHANG Yuanbo;LIU Xiaotian(School of Computer Science,Xi'an Shiyou University,Xi'an 710065,China)

机构地区：[1]西安石油大学计算机学院,陕西西安710065

出　　处：《现代信息科技》2025年第7期52-57,共6页Modern Information Technology

摘　　要：针对实际交通场景下车辆跟踪算法复杂度高、计算量大,难以在资源有限的设备中应用的问题,提出一种基于改进YOLOv8+DeepSORT的多目标车辆跟踪算法。在骨干网络中引入轻量化的GhostNet卷积神经网络,将Conv替换为GhostConv,在保证模型轻量化的同时实现性能的提升。其次通过引入CBAM注意力机制并结合Ghost卷积技术,构建一种新的GC-C2f特征融合模块,进一步增强特征提取的能力。最后使用新的损失函数WIoU,提高模型回归精度和收敛速度。使用改进YOLOv8模型的检测结果作为DeepSORT算法的输入,实现对复杂情况下的多目标车辆跟踪。实验结果表明,在交通数据集KITTI上,GCW-YOLO+DeepSORT在检测精度没有损失的情况下,相较于原YOLOv8+DeepSORT参数量降低35.94%,计算量降低20.25%,更适合在资源有限的设备上部署,具有实用价值。Aiming at the issues that vehicle tracking algorithms are highly complex and computationally intensive in practical traffic scenarios,making it difficult to apply them to devices with limited resources,this paper proposes a multitarget vehicle tracking algorithm based on improved YOLOv8+DeepSORT.A lightweight GhostNet Convolutional Neural Network is introduced into the backbone network,and the Conv is replaced with GhostConv.This replacement not only ensures the lightweight nature of the model but also improves its performance.Subsequently,by introducing the CBAM and integrating it with the Ghost convolution technology,a new GC-C2f feature fusion module is constructed to further enhance the feature extraction ability.Finally,a new loss function,WIoU,is adopted to improve the model's regression accuracy and convergence speed.The detection results of the improved YOLOv8 model are used as the input of the DeepSORT algorithm to achieve multi-target vehicle tracking in complex situations.Experimental results demonstrate that on the KITTI traffic dataset,without sacrificing detection accuracy,compared with the original YOLOv8+DeepSORT,the parameter count of GCWYOLO+DeepSORT is reduced by 35.94%and the computational load is decreased by 20.25%.This makes it more suitable for deployment on devices with limited resources and endows it with practical value.

关 键 词：车辆目标跟踪 YOLOv8 DeepSORT Ghost卷积 轻量型 CBAM 损失函数 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TP301.6[自动化与计算机技术—计算机科学与技术]