Patents by Inventor Yuanbin SHI

Yuanbin SHI 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).

  • Feature fusion and dense connection-based method for infrared plane object detection
    Patent number: 11055574
    Abstract: A feature fusion and dense connection-based method for infrared plane object detection includes: constructing an infrared image dataset containing an object to be recognized, calibrating a position and class of the object to be recognized in the infrared image dataset, and obtaining an original known label image; dividing the infrared image dataset into a training set and a validation set; performing image enhancement preprocessing on images in the training set, performing feature extraction and feature fusion, and obtaining classification results and bounding boxes through a regression network; calculating a loss function according to the classification results and the bounding boxes in combination with the original known label image, and updating parameter values of a convolutional neural network; repeating the steps to iteratively update the parameters of the convolutional neural network; and processing images in the validation set through the parameters to obtain a final object detection result map.
    Type: Grant
    Filed: November 20, 2018
    Date of Patent: July 6, 2021
    Assignee: XIDIAN UNIVERSITY
    Inventors: Huixin Zhou, Jiajia Zhang, Yuanbin Shi, Dong Zhao, Lixin Guo, Hanlin Qin, Bingjian Wang, Rui Lai, Huan Li, Jiangluqi Song, Bo Yao, Yue Yu, Xiuping Jia, Jun Zhou
  • FEATURE FUSION AND DENSE CONNECTION-BASED METHOD FOR INFRARED PLANE OBJECT DETECTION
    Publication number: 20210174149
    Abstract: A feature fusion and dense connection-based method for infrared plane object detection includes: constructing an infrared image dataset containing an object to be recognized, calibrating a position and class of the object to be recognized in the infrared image dataset, and obtaining an original known label image; dividing the infrared image dataset into a training set and a validation set; performing image enhancement preprocessing on images in the training set, performing feature extraction and feature fusion, and obtaining classification results and bounding boxes through a regression network; calculating a loss function according to the classification results and the bounding boxes in combination with the original known label image, and updating parameter values of a convolutional neural network; repeating the steps to iteratively update the parameters of the convolutional neural network; and processing images in the validation set through the parameters to obtain a final object detection result map.
    Type: Application
    Filed: November 20, 2018
    Publication date: June 10, 2021
    Applicant: XIDIAN UNIVERSITY
    Inventors: Huixin ZHOU, Jiajia ZHANG, Yuanbin SHI, Dong ZHAO, Lixin GUO, Hanlin QIN, Bingjian WANG, Rui LAI, Huan LI, Jiangluqi SONG, Bo YAO, Yue YU, Xiuping JIA, Jun ZHOU