Vehicle tire (tyre) detection and text recognition using deep learning

Wajahat Kazmi, Ian Nabney, George Vogiatzis, Peter Rose and Alexander Codd

2019

Abstract

This paper presents an industrial system to read text on tire sidewalls. Images of vehicle tires in motion are acquired using roadside cameras. Firstly, the tire circularity is detected using Circular Hough Transform (CHT) with dynamic radius detection. The tire 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 tire 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 system presents impressive accuracy and efficiency proving its suitability for the intended industrial application.

Citation

Wajahat Kazmi, Ian Nabney, George Vogiatzis, Peter Rose and Alexander Codd. “Vehicle tire (tyre) detection and text recognition using deep learning.” 2019.

@inproceedings{kazmi2019,
  title     = {Vehicle tire (tyre) detection and text recognition using deep learning},
  author    = {Wajahat Kazmi and Ian Nabney and George Vogiatzis and Peter Rose and Alexander Codd},
  pages     = {1074--1079},
  year      = {2019},
  doi       = {10.1109/coase.2019.8842962},
}