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作 者:曾亮 彭龑 ZENG Liang;PENG Yan(School of Computer Science and Engineering,Sichuan University of Science&Engineering,Yibin 643002,China)
机构地区:[1]四川轻化工大学计算机科学与工程学院,四川宜宾643002
出 处:《洛阳理工学院学报(自然科学版)》2024年第3期62-69,共8页Journal of Luoyang Institute of Science and Technology:Natural Science Edition
基 金:自贡市科技局科技计划资助项目(2018GYCX33).
摘 要:针对复杂背景环境下马铃薯叶片病害检测精度低的问题,以YOLOv8n为原型,提出了一种非受控环境下的马铃薯叶片病害检测算法:YOLOv8n-Potato。采用CAA-HS-FPN架构替换YOLOv8的颈部网络,用于提高特征融合效率;使用轻量化检测头Sc-Head替换YOLOv8的检测头,使模型轻量化;采用PIoU替换CIoU,降低了锚框回归的代价。与YOLOv8n相比,YOLOv8n-Potato的精确度提高了2.4%,召回率提高了8.4%,mAP50提高了3.6%,mAP50-95提高了1%,GFLOPs减少了23%,模型参数量减少了42%。To address the issue of low detection accuracy of potato leaf diseases in complex background environments,a potato leaf disease detection algorithm,named YOLOv8n-Potato,was proposed based on YOLOv8n.The algorithm replaces the neck network of YOLOv8 with the CAA-HS-FPN architecture to enhance feature fusion efficiency.Additionally,a lightweight detection head,Sc-Head,is used to replace the detection head of YOLOv8,making the model lightweight.Finally,PIoU is adopted to replace CIoU to reduce the cost of anchor box regression.Compared to YOLOv8n,YOLOv8n-Potato is 2.4%higher in accuracy,8.4%higher in recall rate,3.6%in mAP50,and 1%in mAP50-95,while GFLOPs reduced by 23%and model parameters by 42%.
关 键 词:YOLOv8 高层筛选特征金字塔网络 轻量化检测头 Powerful-IoU
分 类 号:TP391.4[自动化与计算机技术—计算机应用技术] S435.32[自动化与计算机技术—计算机科学与技术]
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