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您的检索式：作者名="Kaizhen Chen"
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| 1 | Easy domain adaptation method for filling the species gap in deep learning-based fruit detection显示文摘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. | Wenli Zhang Kaizhen Chen Jiaqi Wang Yun Shi Wei Guo | 2021 | Horticulture Research2021，8，1： | 2 |
| 2 | EasyDAM_V2: Efficient Data Labeling Method for Multishape, Cross-Species Fruit Detection显示文摘In modern smart orchards,fruit detection models based on deep learning require expensive dataset labeling work to support the construction of detection models,resulting in high model application costs.Our previous work combined generative adversarial networks(GANs)and pseudolabeling methods to transfer labels from one specie to another to save labeling costs.However,only the color and texture features of images can be migrated,which still needs improvement in the accuracy of the data labeling. | Wenli Zhang Kaizhen Chen Chao Zheng Yuxin Liu Wei Guo | 2022 | Plant Phenomics2022，4，1： | 0 |
| 3 | Monitoring the pH fluctuation of lysosome under cell stress using a near-infrared ratiometric fluorescent probe显示文摘Cell stress responses are associated with numerous diseases including diabetes, neurodegenerative diseases, and cancer. Several events occur under cell stress, in which, are protein expression and organellespecific pH fluctuation. To understand the lysosomal pH variation under cell stress, a novel NIR ratiometric pH-responsive fluorescent probe(BLT) with lysosomes localization capability was developed.The quinoline ring of BLT combined with hydrogen ion which triggered the rearrangement of π electrons conjugated at low pH medium, meanwhile, the absorption and fluorescent spectra of BLT showed a red-shifts, which gived a ratiometric signal. Moreover, the probe BLT with a suitable p Kavalue has the potential to discern changes in lysosomal pH, either induced by heat stress or oxidative stress or acetaminophen-induced(APAP) injury stress. Importantly, this ratiometric fluorescent probe innovatively tracks pH changes in lysosome in APAP-induced liver injury in live cells, mice, and zebrafish. The probe BLT as a novel fluorescent probe possesses important value for exploring lysosomal-associated physiological varieties of drug-induced hepatotoxicity. | Lijuan Gui Kaizhen Wang Yuxin Wang Jun Yan Xian Liu Jingxuan Guo Ji Liu Dawei Deng Haiyan Chen Zhenwei Yuan | 2023 | Chinese Chemical Letters2023，34，3： | 0 |
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