Patents by Inventor MIN-XIN CHEN

MIN-XIN CHEN has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Method for constructing a convolution neural network based on farmland images, electronic device using the same
    Patent number: 12013917
    Abstract: A method for constructing a convolutional neural network model based on farmland images is applied in an electronic device. The method includes following steps: obtaining a number of farmland images of at least one farmland; obtaining a plurality of standard segmentation farmland images corresponding to each of the farmland images; dividing the farmland images and the standard segmentation farmland images into a training image set and a test image set; taking the farmland images and the standard segmentation farmland images in the training image set as input of a convolutional neural network, and constructing a convolutional neural network model based on the farmland images; and verifying the convolutional neural network model by using the farmland images in the test image set and the standard segmentation farmland images, and optimizing a plurality of parameters of the convolutional neural network model based on the farmland images.
    Type: Grant
    Filed: November 15, 2021
    Date of Patent: June 18, 2024
    Assignee: FJ Dynamics Technology Co., Ltd
    Inventors: Di Wu, Zhao-Di Chen, Min-Xin Chen, Chen-Jian Wu, Hong Chen
  • METHOD FOR CONSTRUCTING A CONVOLUTION NEURAL NETWORK BASED ON FARMLAND IMAGES, ELECTRONIC DEVICE USING THE SAME
    Publication number: 20220076068
    Abstract: A method for constructing a convolutional neural network model based on farmland images is applied in an electronic device. The method includes following steps: obtaining a number of farmland images of at least one farmland; obtaining a plurality of standard segmentation farmland images corresponding to each of the farmland images; dividing the farmland images and the standard segmentation farmland images into a training image set and a test image set; taking the farmland images and the standard segmentation farmland images in the training image set as input of a convolutional neural network, and constructing a convolutional neural network model based on the farmland images; and verifying the convolutional neural network model by using the farmland images in the test image set and the standard segmentation farmland images, and optimizing a plurality of parameters of the convolutional neural network model based on the farmland images.
    Type: Application
    Filed: November 15, 2021
    Publication date: March 10, 2022
    Inventors: Di Wu, ZHAO-DI CHEN, MIN-XIN CHEN, CHEN-JIAN WU, HONG CHEN