An industrial system for vehicle tyre detection and text recognition using a pipeline of conventional image processing and deep learning

Wajahat Kazmi, Ian Nabney, George Vogiatzis, Peter Rose, Alex Codd

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

This paper presents an industrial system to read
text on tyre sidewalls. Images of vehicle tyres in motion are
acquired using roadside cameras. Firstly, the tyre circularity is
detected using Circular Hough Transform (CHT) with dynamic
radius detection. The tyre is then unwarped into a rectangular
patch and a cascade of convolutional neural network (CNN)
classifiers is applied for text recognition. We introduce a novel
proposal generator for localizing the tyre code by combining
Histogram of Oriented Gradients (HOG) with 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 end-to-end system presents
impressive accuracy and efficiency proving its suitability for the
intended industrial application.
Original languageEnglish
Title of host publication2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)
PublisherIEEE Computer Society
Pages1074-1079
Number of pages6
Publication statusPublished - 26 Aug 2019
EventInternational Conference on Automation Science and Engineering - Vancouver, Canada
Duration: 22 Aug 201926 Aug 2019
Conference number: 15
http://case2019.hust.edu.cn/

Conference

ConferenceInternational Conference on Automation Science and Engineering
Abbreviated titleCASE 2019
CountryCanada
CityVancouver
Period22/08/1926/08/19
Internet address

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