An Efficient Industrial System for Vehicle Tyre (Tire) Detection and Text Recognition Using Deep Learning

Abstract

This paper addresses the challenge of reading low contrast text on tyre sidewall images of vehicles in motion. It presents first of its kind, a full scale industrial system which can read tyre codes when installed along driveways such as at gas stations or parking lots with vehicles driving under 10 mph. Tyre circularity is first detected using a circular Hough transform with dynamic radius detection. The detected tyre arches are then unwarped into rectangular patches. A cascade of convolutional neural network (CNN) classifiers is then applied for text recognition. Firstly, a novel proposal generator for the code localization is introduced by integrating convolutional layers producing HOG-like (Histogram of Oriented Gradients) features into a CNN. The proposals are then filtered using a deep network. After the code is localized, character detection and recognition are carried out using two separate deep CNNs. The results (accuracy, repeatability and efficiency) are impressive and show promise for the intended application.

Publication DOI: https://doi.org/10.1109/TITS.2020.2967316
Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Intelligent vehicles,Optical Character Recognition (OCR),computer vision,deep learning,tyre (tire) sidewall,Automotive Engineering,Mechanical Engineering,Computer Science Applications
Publication ISSN: 1558-0016
Last Modified: 25 Mar 2024 08:58
Date Deposited: 31 Jan 2020 09:39
Full Text Link:
Related URLs: https://ieeexpl ... cument/8968735/ (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-02
Published Online Date: 2020-01-24
Accepted Date: 2019-12-16
Authors: Kazmi, Wajahat
Nabney, Ian
Vogiatzis, George (ORCID Profile 0000-0002-3226-0603)
Rose, Peter
Codd, Alex

Download

[img]

Version: Accepted Version

| Preview

Export / Share Citation


Statistics

Additional statistics for this record