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

Claim 3:
4. A fault detection system for tunnel dome lights based on an improved localization loss function, comprising:
a dataset construction module, configured to construct a dataset of tunnel dome light detection images;
a neural network acquisition module, configured to acquire a you only look once (YOLO) v5s neural network based on the improved localization loss function;
a training module, configured to train the YOLO v5s neural network according to the dataset to obtain a trained YOLO v5s neural network;
a to-be-detected image acquisition module, configured to acquire a to-be-detected tunnel dome light image;
a position detection module, configured to detect, with the trained YOLO v5s neural network, the to-be-detected tunnel dome light image to obtain position coordinates of luminous dome lights; and
a fault recognition module, configured to determine, according to the position coordinates of the luminous dome lights, whether a fault occurs in the tunnel dome lights;, wherein the dataset construction module specifically comprises:
a video acquisition unit, configured to acquire videos of the tunnel dome lights with a multi-angle camera;
a frame extraction unit, configured to extract video frames from acquired videos at intervals to obtain a tunnel dome light image set;
an image labeling unit, configured to label tunnel dome lights in the tunnel dome light image set with Labelling software; and
a format conversion unit, configured to process labeled tunnel dome light images into a Pascal VOC format to obtain the dataset of the tunnel dome light detection 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, the 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.