Portable IoT device for tire text code identification via integrated computer vision system

Abstract

The identification of tire text codes (TTC) during the production and operational phases of tires can significantly improve safety and maintenance practices. Current methods for TTC identification face challenges related to stability, computational efficiency, and outdoor applicability. This paper introduces an automated TTC identification system founded on a robust framework that is both user-friendly and easy to implement, thereby enhancing the practical use and industrial applicability of TTC identification technologies. Initially, instance segmentation is creatively utilized for detecting TTC regions on the tire sidewall through You Only Look Once (YOLO)-v8-based models, which are trained on a dataset comprising 430 real-world tire images. Subsequently, a computationally efficient rotation algorithm, along with specific image pre-processing techniques, is developed to tackle common issues associated with centripetal rotation in the TTC region and to improve the accuracy of TTC region detection. Furthermore, a series of YOLO-v8 object detection models were assessed using an independently collected dataset of 1127 images to optimize the recognition of TTC characters. Ultimately, a portable Internet of Things (IoT) vision device is created, featuring a comprehensive workflow to support the proposed TTC identification framework. The TTC region detection model achieves a segmentation precision of 0.8812, while the TTC recognition model reaches a precision of 0.9710, based on the datasets presented in this paper. Field tests demonstrate the system's advancements, reliability, and potential industrial significance for practical applications. The IoT device is shown to be portable, cost-effective, and capable of processing each tire in 200 ms.