Open AccessReal-time Signal Light Detection based on Yolov5 for Railway
Author(s) -
Wentao Li,
Zhangyu Wang,
Bin Zhou,
Songyue Yang,
Ziren Gong
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/769/4/042069
Subject(s) - signal (programming language) , computer science , real time computing , hough transform , transformation (genetics) , detection theory , artificial intelligence , telecommunications , detector , image (mathematics) , biochemistry , chemistry , gene , programming language
To improve the safety and efficiency of train operation, autonomous driving train have developed rapidly in recent years. Among them, the signal detection is one of the most basic functions. However, due to the small size of signal light and the complicated of the railway environment, the signal detection is still a huge problem. The existing methods, such as the approach based on Hough circle transformation, are hard to meet the practical application requirements. In this paper, a real time railway signal lights detection based on Yolov5 is introduced. And a lot of experiments were conducted to prove the effectiveness of the proposed method. The experimental results show that the proposed method achieved 0.972 for both average recall rate and average accuracy rate. Besides, the detection speed of the proposed method reached astonishing 100FPS. Overall, the detection speed and accuracy both meet the practical application requirements.