基于改进YOLOv8s的杭白菊检测与花期分类  

Detecting chrysanthemum to classify flowering stages using improved YOLOv8s

作　　者：施国英[1] 纪嘉鹏 李天华[1] 李文显 李扬[1] 张观山[1] SHI Guoying;JI Jiapeng;LI Tianhua;LI Wenxian;LI Yang;ZHANG Guanshan(College of Mechanical and Electronic Engineering,Shandong Agricultural University,Tai’an 271018,China)

机构地区：[1]山东农业大学机械与电子工程学院,泰安271018

出　　处：《农业工程学报》2025年第7期192-199,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：山东省重点研发计划乡村振兴科技创新提振行动计划项目(2023TZXD065);山东蔬菜产业技术体系项目(SDAIT-05-11)。

摘　　要：为精准识别与分类不同花期杭白菊,满足自动化采摘要求,该研究提出一种基于改进YOLOv8s的杭白菊检测模型-YOLOv8s-RDL。首先,该研究将颈部网络(neck)的C2f(faster implementation of CSP bottleneck with 2 convolutions)模块替换为RCS-OSA(one-shot aggregation of reparameterized convolution based on channel shuffle)模块,以提升骨干网络(backbone)特征融合效率;其次,将检测头更换为DyHead(dynamic head),并融合DCNv3(deformable convolutional networks v3),借助多头自注意力机制增强目标检测头的表达能力;最后,采用LAMP(layer-adaptive magnitude-based pruning)通道剪枝算法减少参数量,降低模型复杂度。试验结果表明,YOLOv8s-RDL模型在菊米和胎菊的花期分类中平均精度分别达到96.3%和97.7%,相较于YOLOv8s模型,分别提升了3.8和1.5个百分点,同时权重文件大小较YOLOv8s减小了6 MB。该研究引入TIDE(toolkit for identifying detection and segmentation errors)评估指标,结果显示,YOLOv8s-RDL模型分类错误和背景检测错误相较YOLOv8s模型分别降低0.55和1.26。该研究为杭白菊分花期自动化采摘提供了理论依据和技术支撑。Chrysanthemum tea has been one of the most popular food products,due to the health benefits and high commercial value.The medicinal and economic chrysanthemum can greatly vary in the different flowering stages.Among them,the flowering stages of chrysanthemum can be categorized into the Jumi(flower buds),Taiju(flower buds just before blooming),and Duohua(fully bloomed flowers).At the same time,the chrysanthemum is required for the best picking time.However,manual picking cannot fully meet the requirement of large-scale production at the early flowering stage,due mainly to the laborintensive and time-consuming.Untimely picking or picking errors at the different flowering stages can also lead to the waste of chrysanthemum,even the serious economic losses.Therefore,the picking robot can be expected to realize the accurate and rapid recognition of chrysanthemum in different flowering stages using lightweight model.In this study,an improved YOLOv8s model(YOLOv8s-RDL)was proposed for the object detection of chrysanthemum.Firstly,the C2f(faster implementation of CSP bottleneck with 2 convolutions)in Neck network was replaced by RCS-OSA(one-shot aggregation of reparameterized convolution using channel shuffle).The features were extracted to more efficiently fuse by the Backbone layer;Secondly,the decoupled head was replaced with the Dyhead(dynamic head),and then integrated into the DCNv3(deformable convolutional networks v3).The multi-head self-attention mechanism was combined to strengthen the expression of the target detection head.Finally,the LAMP(layer-adaptive magnitude-based pruning)was used to reduce the number of parameters and the complexity of the model network.The amount of calculation was significantly reduced to maintain a high mean average precision level of the improved model.A comparison was also made to explore the effect of RCS-OSA in the different positions of the network.The performance of the model was depended mainly on the different pruning conditions and rates in the same pruning direction.The best

关 键 词：图像识别 YOLOv8s 杭白菊检测 花期分类 LAMP 

分 类 号：S24[农业科学—农业电气化与自动化]