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.
|Name||2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)|
|Conference||2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)|
|Period||22/08/19 → 26/08/19|
Funding: Innovate UK under the Knowledge Transfer Partnership (KTP) Grant No. KTP009834.