An industrial system for vehicle tyre detection and text recognition using a pipeline of conventional image processing and deep learning
Bristol Research (University of Bristol) · 2019
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.
Citation
Wajahat Kazmi, Ian T. Nabney, George Vogiatzis, Peter Rose and Alex Codd. “An industrial system for vehicle tyre detection and text recognition using a pipeline of conventional image processing and deep learning.” Bristol Research (University of Bristol), pp. 1074–1079. 2019.
BibTeX
@article{kazmi2019,
title = {An industrial system for vehicle tyre detection and text recognition using a pipeline of conventional image processing and deep learning},
author = {Wajahat Kazmi and Ian T. Nabney and George Vogiatzis and Peter Rose and Alex Codd},
journal = {Bristol Research (University of Bristol)},
pages = {1074--1079},
year = {2019},
}