Patent ID: 11908120
Assignee: EAST CHINA JIAOTONG UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A fault detection method for tunnel dome lights based on an improved localization loss function, comprising:
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;, wherein the constructing a dataset of tunnel dome light detection images specifically comprises:
acquiring videos of the tunnel dome lights with a multi-angle camera;
extracting video frames from acquired videos at intervals to obtain a tunnel dome light image set,
labeling tunnel dome lights in the tunnel dome light image set with Labeling software; and
processing labeled tunnel dome light images into a Pascal VOC format to obtain the dataset of the tunnel dome light detecting images;
the improved localization loss function is specifically a side, corner and aspect ratio loss for bounding box regression (SCALoss) function, and comprises a side overlap (SO) loss, a corner distance (CD) loss and an aspect ratio (AR) loss; and,
the SCALoss function has a following Eq:

LSCA=LSO+αLCD+LAR 

wherein, LSCA is the SCALoss function, LSO is the SO loss, LCD is the CD loss, LAR is the AR loss, and α is a weight coefficient.