Patents by Inventor Xiangkui HE

Xiangkui HE 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).

  • Detection method and detection device for detecting fault of insulator discharge based on image recognition
    Patent number: 12718569
    Abstract: A detection method for detecting fault of insulator discharge based on image recognition includes the following steps: building an ultraviolet image dataset for an electrical equipment, building a neural network model for insulator discharge detection based on YOLOV5-T, the neural network model includes a CBS module, a CST module, a Concat module, a 1D-attention module, an Upsample module and a Detect module, training the neural network model by utilizing the ultraviolet image dataset for the electrical equipment, to obtain a trained neural network model, and inputting images to be recognized into the trained neural network model, to obtain fault detection results. A detection device is configured to implement the detection method.
    Type: Grant
    Filed: July 9, 2024
    Date of Patent: August 25, 2026
    Assignees: State Grid Hubei Extra High Voltage Company, Hubei Superenergic Electric Power Co., Ltd.
    Inventors: Jun Wu, Shenli Wang, Xiangdong Wu, Xiangkui He, Quan Fang, Huaqiao Xiang, Xun Zhang, Xiaohui Wei
  • DETECTION METHOD AND DETECTION DEVICE FOR DETECTING FAULT OF INSULATOR DISCHARGE BASED ON IMAGE RECOGNITION
    Publication number: 20250139972
    Abstract: A detection method for detecting fault of insulator discharge based on image recognition includes the following steps: building an ultraviolet image dataset for an electrical equipment, building a neural network model for insulator discharge detection based on YOLOV5-T, the neural network model includes a CBS module, a CST module, a Concat module, a 1D-attention module, an Upsample module and a Detect module, training the neural network model by utilizing the ultraviolet image dataset for the electrical equipment, to obtain a trained neural network model, and inputting images to be recognized into the trained neural network model, to obtain fault detection results. A detection device is configured to implement the detection method.
    Type: Application
    Filed: July 9, 2024
    Publication date: May 1, 2025
    Inventors: Jun WU, Shenli WANG, Xiangdong WU, Xiangkui HE, Quan FANG, Huaqiao XIANG, Xun ZHANG, Xiaohui WEI