Patents by Inventor Cailing Tang

Cailing Tang 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).

  • Fault detection method and system for tunnel dome lights based on improved localization loss function
    Patent number: 11908120
    Abstract: A fault detection method and system for tunnel dome lights based on an improved localization loss function. The method includes: constructing a dataset of tunnel dome light detection images; acquiring a you only look once (YOLO) v5s neural network based on the improved localization loss function; training the YOLO v5s neural network according to the dataset to obtain a trained YOLO v5s neural network; acquiring a to-be-detected tunnel dome light image; detecting, with the trained YOLO v5s neural network, the to-be-detected tunnel dome light image to obtain position coordinates of luminous dome lights; and determining, according to the position coordinates of the luminous dome lights, whether a fault occurs in the tunnel dome lights. The present disclosure can accurately localize the tunnel dome lights and label the positions, and can detect whether the tunnel dome lights work normally according to a relative positional relationship between the labeled dome lights.
    Type: Grant
    Filed: August 25, 2022
    Date of Patent: February 20, 2024
    Assignee: EAST CHINA JIAOTONG UNIVERSITY
    Inventors: Gang Yang, Cailing Tang, Lizhen Dai, Zhipeng Yang, Yuyu Weng, Hui Yang, Rongxiu Lu, Fangping Xu, Shuo Xu
  • FAULT DETECTION METHOD AND SYSTEM FOR TUNNEL DOME LIGHTS BASED ON IMPROVED LOCALIZATION LOSS FUNCTION
    Publication number: 20230368354
    Abstract: A fault detection method and system for tunnel dome lights based on an improved localization loss function. The method includes: constructing a dataset of tunnel dome light detection images; acquiring a you only look once (YOLO) v5s neural network based on the improved localization loss function; training the YOLO v5s neural network according to the dataset to obtain a trained YOLO v5s neural network; acquiring a to-be-detected tunnel dome light image; detecting, with the trained YOLO v5s neural network, the to-be-detected tunnel dome light image to obtain position coordinates of luminous dome lights; and determining, according to the position coordinates of the luminous dome lights, whether a fault occurs in the tunnel dome lights. The present disclosure can accurately localize the tunnel dome lights and label the positions, and can detect whether the tunnel dome lights work normally according to a relative positional relationship between the labeled dome lights.
    Type: Application
    Filed: August 25, 2022
    Publication date: November 16, 2023
    Applicant: East China Jiaotong University
    Inventors: Gang Yang, Cailing Tang, Lizhen Dai, Zhipeng Yang, Yuyu Weng, Hui Yang, Rongxiu Lu, Fangping Xu, Shuo Xu