The 4th International Conference on Electronics, Communications and Control Engineering 2021
DOI: 10.1145/3462676.3462686
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Corn Ear Quality Recognition Based on DCGAN Data Enhancement and Transfer Learning

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Cited by 3 publications
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“…Where G is the generative network, D is the discriminant network, 𝐺(𝑧) is the image obtained by the generator, 𝐷(𝐺(𝑧)) is the discrimination probability of the generated image, and 𝐷(𝑥) is the discrimination probability of the real image [15].…”
Section: B Improved Dcgan Loss Functionmentioning
confidence: 99%
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“…Where G is the generative network, D is the discriminant network, 𝐺(𝑧) is the image obtained by the generator, 𝐷(𝐺(𝑧)) is the discrimination probability of the generated image, and 𝐷(𝑥) is the discrimination probability of the real image [15].…”
Section: B Improved Dcgan Loss Functionmentioning
confidence: 99%
“…The dataset images are then randomly divided into two groups: 80% of the dataset is used for parameter learning and network training, whereas the other 20% is used to test the generalization and recognition ability of the model, and the two datasets do not intersect each other. (15,30), (13,48), (19,38), (17,59), (14,81), (17,75), (24,55), (19,100), (14,144). The distribution is shown in Table 2.…”
Section: A Preparation Of Adhesive Structures Defect Datasetmentioning
confidence: 99%
“…In recent years, the research of plant phenotypes based on image semantic segmentation has been playing an important role. However, the current research mainly focuses on the classification of abnormal regions of the whole ear, and has not achieved the fine segmentation of abnormal regions of the single ear [5,6]. Most researchers extracted the image colour, texture, shape and other features [7,8], and further used the image segmentation algorithm for implementation.…”
Section: Related Workmentioning
confidence: 99%
“…Custom CNN models provided improved results compared to transfer learning and they gives an accuracy of 97.37% ( 20 ). The Dense Attentive Circular Network is used for malaria cell detection and achieved results are compared to the transfer learning models such as DPN92 and DenseNet121.This method provided an accuracy of 97.47 and 87.88% on DenseNet121 and DPN-92, respectively ( 21 ). The pre-trained networks such as ResNet50, AlexNet, and VGG19 are used for malaria classification and provide an accuracy of 93.88, 96.33, and 93.72%, respectively.…”
Section: Related Workmentioning
confidence: 99%