基于改进YOL0v8+DeepS0RT的多目标车辆跟踪算法研究

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关键词：车辆目标跟踪；YOLOv8；DeepSORT；Ghost卷积；轻量型；CBAM；损失函数中图分类号：TP391.4；TP301.6 文献标识码：A 文章编号：2096-4706（2025）07-0052-06

Abstract：Aiming at the issues that vehicle tracking algorithms are highlycomplex and computationally intensive in practical traficscenarios，making it diffcult toapplythem todevices withlimited resources，this paper proposesamultitargetvehicletrackingalgorithmbasedonimproved YOLOv8 + DeepSORT.AlightweightGhostNet ConvolutionalNeural Network is introduced intothe backbone network，and the Conv is replaced with GhostConv.This replacement not only ensures the lightweight natureof themodel but also improves its performance.Subsequently，by introducing the CBAMand integrating it with the Ghost convolution technology，anew GC-C2ffeature fusion module is constructed to further enhance the featureextractionability.Finallyanewlossunction，WIoU，isadoptedtoimprovethmodel'segreionacacyand convergence speed.The detectionresults ofthe improved YOLOv8 modelare usedasthe input ofthe DepSORTalgorithm toachievemulti-targetvehicletracking incomplexsituations.Experimentalresultsdemonstratethaton theKITTItraffic dataset， withoutsacrificing detection accuracy，compared with theoriginal YOLOv8+DeepSORT，the parametercountof GCWYOLO+DeepSORT is reduced by 3 5 . 9 4 % and the computational load is decreased by 2 0 . 2 5 % .This makes it more suitable for deployment on devices with limited resources and endows it with practical value.

KeyWords：vehicle target tracking; YOLOv8; DeepSORT; Ghost convolution; Lightweight type; CBAM; Loss Functic

0 引言

在国家政策的引导和大力支持下，我国计算机视觉技术迎来了快速发展的黄金时期。（剩余8692字）