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


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:
Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences
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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: 23 Jul 2024 07:08
Date Deposited: 31 Jan 2020 09:39
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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



Version: Accepted Version

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