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自然复杂环境下油茶果识别的重参数化算法

Xiao Shenping Deng Hongjin Zhao Qianying Chen Yongzhong

中国农机化学报2026，Vol.47Issue(2)：78-85,8.

中国农机化学报2026，Vol.47Issue(2)：78-85,8.DOI:10.13733/j.jcam.issn.2095-5553.2026.02.012

自然复杂环境下油茶果识别的重参数化算法

RepVGG algorithm for Camellia oleifera fruits recognition in natural complex environment

Xiao Shenping ¹Deng Hongjin ²Zhao Qianying ²Chen Yongzhong³

作者信息

1. School of Electrical and Information Engineering,Hunan University of Technology,Zhuzhou,412007,China||Key Laboratory for Electric Drive Control and Intelligent Equipment of Hunan Province,Zhuzhou,412007,China
2. Key Laboratory for Electric Drive Control and Intelligent Equipment of Hunan Province,Zhuzhou,412007,China||School of Railway Transportation,Hunan University of Technology,Zhuzhou,412007,China
3. National Engineering Research Center for Oil Tea Camellia,Hunan Academy of Forestry,Changsha,410100,China
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摘要

Abstract

To address the challenges in the machine picking recognition task of Camellia oleifera fruits in natural environments,such as dense fruit adhesion,leaf and branch occlusion,fruit color difference,and uneven lighting,and in light of the current research issues of insufficient detection accuracy and robustness in complex scenarios,an improved YOLOv8n model,namely YOLOv8—COD,is proposed.In this model,hyper parameters in C2f module are adjusted and lightweight convolutional module is integrated.Heavy parameterization module is used to replace convolutional module in backbone network,so as to maintain computational efficiency while improving model detection accuracy.Adding Global Attention Mechanism(GAM)into the feature fusion module and replacing CIoU with GIoU—Focal can help the model focus on camellia fruit and improve the recognition rate of the model under the conditions of fruit occlusion and adhesion.Compared with the traditional YOLOv8n,its precision rate,recall rate,and mAP are increased by 0.2%,3.3%,and 2.1%respectively.In complex natural environment,the missed detection probability of YOLOv8—COD decreased significantly compared with YOLOv8n,and the detection accuracy was improved,which can effectively realize the detection and identification of Camellia oleifera fruits.

关键词

油茶果/YOLOv8n/检测识别/YOLOv8—COD/重参数化

Key words

Camellia oleifera fruits/YOLOv8n/detection and recognition/YOLOv8—COD/RepVGG

分类

农业科技

引用本文复制引用

Xiao Shenping,Deng Hongjin,Zhao Qianying,Chen Yongzhong..自然复杂环境下油茶果识别的重参数化算法[J].中国农机化学报,2026,47(2):78-85,8.

基金项目

国家重点研发计划项目(2019YFE0122600) （2019YFE0122600）

2024 年度十大技术攻关项目(2024NK2020) （2024NK2020）

中国农机化学报

ISSN：2095-5553

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