基于改进YOLOv8s的柑橘果实成熟度检测  

Detecting citrus fruit maturity using improved YOLOv8s

作　　者：李莹[1] 刘梦莲 何自芬[1] 娄杨伟 LI Ying;LIU Menglian;HE Zifen;LOU Yangwei(School of Electromechanical Engineering,Kunming University of Science and Technology,Kunming 650500,China)

机构地区：[1]昆明理工大学机电工程学院,昆明650500

出　　处：《农业工程学报》2024年第24期157-164,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(62171206)。

摘　　要：为了解决柑橘果实被枝叶和其他果实遮挡以及不同果实之间的成熟情况不同等导致识别果实成熟度困难的问题,该研究提出一种改进YOLOv8s模型的柑橘果实成熟度检测方法。首先,在网络主干中添加混合注意力变换器模块(hybrid attention transformer,HAT),使模型利用更多的像素信息以提高对果实成熟度特征的表征能力;然后,将检测头替换为四头的自适应空间特征融合检测头(four-adaptively spatial feature fusion,FASFF)实现果实成熟度特征的尺度不变性。改进YOLOv8s模型在测试集上的精确度P为94.8%,召回率R为88.6%,平均精度均值mAP_(0.5)和mAP_(0.5~0.95)分别为95.6%和88.9%,模型参数量为21.51 M,检测速度为52.9帧/s。比Faster-RCNN、YOLOv3-spp、YOLOv7和原始YOLOv8s模型精确度分别提升26.0、5.8、4.8和5.5个百分点,平均精度均值mAP_0.5分别提升0.5、1.7、1.6和1.6个百分点。改进YOLOv8s模型能够为柑橘采摘机器人的选择性采收提供有效支持。Accurate and automatic identification of citrus fruit ripeness can greatly contribute to the fruit flavor and quality during intelligent harvesting.However,citrus fruits in the natural environment can not ripen at the same time,leading to the varying degree of ripeness on the same tree.It is also difficult to identify the maturity of citrus fruits,due to the occlusion by branches and leaves,as well as the overlapping occlusion among fruits.In this study,the national standard was developed for the pickable maturity of citrus fruits,with reference to the national standard of the Ministry of Agriculture,NY/T 716-2003,and the operating procedure of wide-skinned citrus for post-harvest storage and logistics,GH/T 1336-2021,according to the phenotypic characteristics of citrus fruits during the ripening period.The citrus fruits to be picked were classified into two categories:1)Citrus fruits beyond the harvestable maturity of UM(unpickable maturity,UM);and 2)Citrus fruits reaching pickable maturity,PM(pickable maturity,PM).An improved YOLOv8s model was also proposed to identify the ripeness degree of citrus fruits.Firstly,a hybrid attention transformer(HAT)module was added into the network backbone.The low-quality citrus fruit images were reconstructed into the high-resolution images.More information was utilized to capture the ripeness features of the fruits,thus improving the accuracy of the detection;Secondly,the detection head was replaced with a four-head adaptive spatial feature fusion(FASFF)detection head.Adaptive spatial fusion weights were learned at different spatial locations.Thus,different levels of citrus fruit features were effectively fused together to improve the scale invariance of features.The improved YOLOv8s model was finally tested by 209 citrus fruit test images.The precision P of improved YOLOv8s model on the test set was 94.8%,the recall R was 88.6%,the mAP_(0.5) and mAP_(0.5~0.95) were 95.6%and 88.9%,respectively.Comparing with the Faster-RCNN,YOLOv3-spp,YOLOv7,and original YOLOv8s models,the pr

关 键 词：柑橘 成熟度 YOLOv8s 混合注意力变换器(HAT) 自动化采摘 

分 类 号：S24[农业科学—农业电气化与自动化] S233.4[农业科学—农业工程]