Automated counting of grape berries has become one of the most important tasks in grape yield prediction.However,dense distribution of berries and the severe occlusion between berries bring great challenges to countin...Automated counting of grape berries has become one of the most important tasks in grape yield prediction.However,dense distribution of berries and the severe occlusion between berries bring great challenges to counting algorithm based on deep learning.The collection of data required for model training is also a tedious and expensive work.To address these issues and cost-effectively count grape berries,a semi-supervised counting of grape berries in the field based on density mutual exclusion(CDMENet)is proposed.The algorithm uses VGG16 as the backbone to extract image features.Auxiliary tasks based on density mutual exclusion are introduced.The tasks exploit the spatial distribution pattern of grape berries in density levels to make full use of unlabeled data.In addition,a density difference loss is designed.The feature representation is enhanced by amplifying the difference of features between different density levels.The experimental results on the field grape berry dataset show that CDMENet achieves less counting errors.Compared with the state of the arts,coefficient of determination(R^(2))is improved by 6.10%,and mean absolute error and root mean square error are reduced by 49.36%and 54.08%,respectively.The code is available at.展开更多
基金supported in part by National Natural Science Foundation of China under Grant 61906139in part by Knowledge Innovation Program of Wuhan-Shuguang Project under Grant 2022010801020359+1 种基金in part by the Hubei Key Laboratory of Intelligent Robot(Wuhan Institute of Technology)of China under Grant HBIRL 202108in part by Graduate Innovative Fund of Wuhan Institute of Technology under Grant CX2022336.
文摘Automated counting of grape berries has become one of the most important tasks in grape yield prediction.However,dense distribution of berries and the severe occlusion between berries bring great challenges to counting algorithm based on deep learning.The collection of data required for model training is also a tedious and expensive work.To address these issues and cost-effectively count grape berries,a semi-supervised counting of grape berries in the field based on density mutual exclusion(CDMENet)is proposed.The algorithm uses VGG16 as the backbone to extract image features.Auxiliary tasks based on density mutual exclusion are introduced.The tasks exploit the spatial distribution pattern of grape berries in density levels to make full use of unlabeled data.In addition,a density difference loss is designed.The feature representation is enhanced by amplifying the difference of features between different density levels.The experimental results on the field grape berry dataset show that CDMENet achieves less counting errors.Compared with the state of the arts,coefficient of determination(R^(2))is improved by 6.10%,and mean absolute error and root mean square error are reduced by 49.36%and 54.08%,respectively.The code is available at.
