Detection and Recognition of Tilted Characters on Railroad Wagon Wheelsets Based on Deep Learning
Author:
Xu Fengxia1ORCID, Xu Zhenyang1, Lu Zhongda1, Peng Chuanshui2, Yan Shiwei2
Affiliation:
1. School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161000, China 2. Qiqihar Sida Railway Equipment Co., Ltd., Qiqihar 161006, China
Abstract
The quality of railroad wheelsets is an important guarantee for the safe operation of wagons, and mastering the production information of wheelsets plays a vital role in vehicle scheduling and railroad transportation safety. However, when using objection detection methods to detect the production information of wheelsets, there are situations that affect detection such as character tilting and unfixed position. Therefore, this paper proposes a deep learning-based method for accurately detecting and recognizing tilted character information on railroad wagon wheelsets. It covers three parts. Firstly, we construct a tilted character detection network based on Faster RCNN for generating a wheelset’s character candidate regions. Secondly, we design a tilted character correction network to classify and correct the orientation of flipped characters. Finally, a character recognition network is constructed based on convolutional recurrent neural network (CRNN) to realize the task of recognizing a wheelset’s characters. The result shows that the method can quickly and effectively detect and identify the information of tilted characters on wheelsets in images.
Funder
Heilongjiang Province Key R&D Program State Grid Corporation Heilongjiang Electric Power Co., Ltd. Fundamental Research Funds in Heilongjiang Provincial Universities Heilongjiang Province Discipline collaborative innovation Achievement Construction Project
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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