TY - GEN
T1 - Vehicle tire (tyre) detection and text recognition using deep learning
AU - Kazmi, Wajahat
AU - Nabney, Ian
AU - Vogiatzis, George
AU - Rose, Peter
AU - Codd, Alexander
N1 - Funding: Innovate UK under the Knowledge Transfer Partnership (KTP) Grant No. KTP009834.
PY - 2019/9/19
Y1 - 2019/9/19
N2 - 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.
AB - 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.
UR - https://ieeexplore.ieee.org/document/8842962/
UR - http://www.scopus.com/inward/record.url?scp=85072958489&partnerID=8YFLogxK
U2 - 10.1109/COASE.2019.8842962
DO - 10.1109/COASE.2019.8842962
M3 - Conference publication
SN - 978-1-7281-0357-0
VL - 2019-August
T3 - 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)
SP - 1074
EP - 1079
BT - 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PB - IEEE
T2 - 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)
Y2 - 22 August 2019 through 26 August 2019
ER -