Easy domain adaptation method for filling the species gap in deep learning-based fruit detection

查看全文 作　　者：Wenli [1]Zhang;Kaizhen [1]Chen;Jiaqi [1]Wang;Yun [2]Shi;Wei [3]Guo

机构地区：[1]Information Department,Beijing University of Technology,Beijing 100022,China;[2]Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing 100081,China;[3]International Field Phenomics Research Laboratory,Institute for Sustainable Agro-Ecosystem Services,Graduate School of Agricultural and Life Sciences,The University of Tokyo,Tokyo 188-0002,Japan

出　　处：《Horticulture Research》索引2021年第8卷第1期,共13页

基　　金：supported by National Natural Science Foundation of China(NSFC)program U19A2061;Japan Science and Technology Agency(JST)CREST program JPMJCR1512,SICORP Program JPMJSC16H2 and aXis program JPMJAS2018. 摘　　要：Fruit detection and counting are essential tasks for horticulture research.With computer vision technology development,fruit detection techniques based on deep learning have been widely used in modern orchards.However,most deep learning-based fruit detection models are generated based on fully supervised approaches,which means a model trained with one domain species may not be transferred to another.There is always a need to recreate and label the relevant training dataset,but such a procedure is time-consuming and labor-intensive.This paper proposed a domain adaptation method that can transfer an existing model trained from one domain to a new domain without extra manual labeling.The method includes three main steps:transform the source fruit image(with labeled information)into the target fruit image(without labeled information)through the CycleGAN network;Automatically label the target fruit image by a pseudo-label process;Improve the labeling accuracy by a pseudo-label self-learning approach.Use a labeled orange image dataset as the source domain,unlabeled apple and tomato image dataset as the target domain,the performance of the proposed method from the perspective of fruit detection has been evaluated.Without manual labeling for target domain image,the mean average precision reached 87.5%for apple detection and 76.9%for tomato detection,which shows that the proposed method can potentially fill the species gap in deep learning-based fruit detection. 关键词：image ORANGE consuming

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Easy domain adaptation method for filling the species gap in deep learning-based fruit detection

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