沈阳农业大学学报2025，Vol.56Issue(6)：45-54,10.DOI:10.3969/j.issn.1000-1700.2025.06.005
基于改进YOLOv10n的轻量化复杂背景葡萄叶片病害检测方法
A Lightweight Detection Method for Grape Leaf Diseases in Complex Backgrounds Based on Improved YOLOv10n
摘要
Abstract
[Objective]To address the challenge of balancing model accuracy and deployment efficiency in grape leaf disease detection under complex backgrounds,this study proposes a lightweight real-time detection model based on an improved YOLOv10n.Through structural optimization and attention mechanism enhancement,the model significantly reduces computational complexity while maintaining detection accuracy,facilitating efficient deployment on mobile devices and in practical agricultural scenarios.[Methods]First,a C2f-HFDRB module was designed in the Backbone network to replace the original C2f module.By splitting input features into high-and low-frequency branches,the modeling capability for high-frequency information and local details of disease regions was enhanced.Second,the CAA-HSFPN structure was adopted to replace the Neck network,achieving efficient feature pyramid fusion by streamlining high-computation components.Finally,the TripletAttention module was integrated to precisely focus on disease target areas in complex backgrounds by capturing cross-dimensional dependencies across spatial and channel dimensions.[Results]The proposed model achieved a precision of 92.0%,an improvement of 1.1%;a recall rate of 91.0%;and a mean average precision(mAP@0.5)of 93.3%,an increase of 1.4%.In terms of computational efficiency,the model's computational load was reduced by 60%to 3.4 GFLOPs,and the number of parameters decreased by 0.95 M to only 1.95 M,demonstrating excellent lightweight characteristics.[Conclusion]Compared with mainstream lightweight detection algorithms,the proposed method exhibits significant advantages in balancing accuracy and efficiency.It provides an effective technical solution and important reference for real-time,accurate detection of grape leaf diseases and practical applications in resource-constrained environments.关键词
yolov10n/葡萄叶片/图像识别/轻量化Key words
YOLOv10n/grape leaves/image recognition/lightweight分类
信息技术与安全科学引用本文复制引用
乔世成,赵晨雨,李成镛,白明宇,党珊珊,潘春宇,张明月..基于改进YOLOv10n的轻量化复杂背景葡萄叶片病害检测方法[J].沈阳农业大学学报,2025,56(6):45-54,10.基金项目
国家自然科学基金项目(62162049) （62162049）
内蒙古民族大学博士科研启动资金项目(BS658) （BS658）
人兽共患病自治区高等学校重点实验室开放基金项目(MDK2022019) （MDK2022019）
内蒙古自治区牧草智能装备创新中心开放基金项目(MDK2025050) （MDK2025050）
内蒙古自治区自然科学基金项目(2025LHMS06012) （2025LHMS06012）
