East China Jiaotong University

Cailing Tang

Rongxiu Lu

Gang Yang

Fangping Xu

Lizhen Dai

Yuyu Weng

Zhipeng Yang

Shuo Xu

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.

Fault detection method and system for tunnel dome lights based on improved localization loss function

Growing community of inventors

Nanchang, China

Cailing Tang