YOLO系列目标检测模型发展规律与未来演进方向研究

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中图分类号：U463.6 文献标识码：A 文章编号：1003-8639（2025）11-0067-03

Research ontheDevelopmentLawandFuture EvolutionDirectionofYOLO Series ObjectDetecti

LiuSijia,HuangJunfeng,LiYuchen,LiFaqi,XuHuimei (Xi'an Traffic Engineering University,Xi'an71O3Oo,China)

【Abstract】The YOLO series model is a representative algorithm for single-stage object detection,and it has undergone five major iterations since itwas proposed in 2O16.This paper systematicallyanalyzes the technical evolution path of YOLO v1 to v5 ,and summarizes the development laws from the dimensions of network architecture design,loss functionoptimization and feature fusion strategy.Suggestions forits future developmentareputforward incombination with the current technical trend.

【Key Words】object detection;YOLO；deep learning;network architecture；loss functic

0 引言

著名的图像目标识别模型系列（YouOnlyLookOnce，YOLO）以其快速而著称，最初由JosephRedmon等人在2016年提出。（剩余6020字）