摘要
为了实现对不同成熟度番茄的实时检测,提出改进YOLOv8s-Seg模型,从而满足现代农业对精准管理的需求。通过改进YOLOv8s-Seg模型的颈部模块来提高其网络性能,在每次上采样操作前,添加1×1 SimConv卷积,将颈部剩余的常规卷积替换为3×3 SimConv卷积,显著提高算法的特征融合能力。结果表明,改进YOLOv8s-Seg模型对成熟番茄、半成熟番茄和未成熟番茄的分割精确率分别为92.7%、92.3%和89.9%。与YOLOv8s-Seg模型相比,改进YOLOv8s-Seg模型的精确率、召回率、F1评分和mAP@0.5分别提高1.6、0.4、1.0、2.4个百分点;改进YOLOv8s-Seg模型的精确率、召回率、F1评分和mAP@0.5均高于YOLOv8s-Seg模型、YOLOv5s-Seg模型、YOLOv7-Seg模型和Mask R-CNN模型;改进YOLOv8s-Seg模型的推理时间为3.5 ms,虽然比YOLOv5s-Seg模型和YOLOv8s-Seg模型略有增加,但明显低于YOLOv7-Seg模型和Mask R-CNN模型。改进YOLOv8s-Seg模型在复杂环境下的番茄成熟度分割任务中表现出优异性能;无论是叶片遮挡、果实重叠,还是光照变化与角度变化,该模型均能实现高精度的分割效果。
To achieve real-time detection of tomato at different maturity stages,an improved YOLOv8s-Seg model was proposed to meet the precision management requirements of modern agriculture.By enhancing the neck module of the improved YOLOv8s-Seg model,a 1×1 SimConv layer was added before each upsampling operation,and the remaining conventional convolutions in the neck were replaced with 3×3 SimConv layers,significantly improving feature fusion capability.The results showed that the improved YO⁃LOv8s-Seg model achieved segmentation precision rates of 92.7%,92.3%,and 89.9%for mature,semi-mature,and immature toma⁃toes,respectively.Compared with the original YOLOv8s-Seg model,the improved YOLOv8s-Seg model demonstrated increases of 1.6,0.4,1.0,and 2.4 percentage points in precision,recall,F1-score,and mAP@0.5,respectively.The improved YOLOv8s-Seg model outperformed YOLOv8s-Seg,YOLOv5s-Seg,YOLOv7-Seg,and Mask R-CNN models in precision,recall,F1-score,and mAP@0.5.The inference time of the improved YOLOv8s-Seg model was 3.5 ms,showing a slight increase compared to YOLOv5s-Seg and YOLOv8s-Seg models,but remained significantly lower than YOLOv7-Seg and Mask R-CNN models.The improved YOLOv8sSeg model exhibited superior performance in tomato maturity segmentation under complex environments,achieving high precision across scenarios involving leaf occlusion,fruit overlap,lighting variations,and viewpoint changes.
作者
杨爽
周中林
YANG Shuang;ZHOU Zhong-lin(Economics and Management School,Yangtze University,Jingzhou 434023,Hubei,China)
出处
《湖北农业科学》
2025年第6期178-184,共7页
Hubei Agricultural Sciences
基金
国家自然科学基金面上项目(62077018)。
