## 1 Introduction
### The Role of AI in Tire Quality Control  
In recent years, AI has emerged as a powerful tool for improving quality control in various industries. The tire industry has recognized the potential of AI in enhancing tire inspections, research and development processes, and real-life tire judgment. By leveraging advanced technologies, machine learning (ML) algorithms, and big data analytics, AI enables tire manufacturers to achieve higher accuracy, efficiency, and reliability in quality control. AI applications, both traditional and deep learning-based, are already slowly taking a key place in the industry of practical measurement systems, especially based on the paradigms of Industry 4.0 and Industry 5.0. In doing so, they focus on several main groups of issues: measurement and de-noising techniques; applications for recognizing and tracking objects and people; solutions for analyzing and predicting environmental conditions; and above all, recommendation, second opinion, and education systems [1]. This does not bypass the tire manufacturing industry either. An example of this is digital shearography supported by deep learning methods used to assess tire quality with automatic detection of defects, especially those invisible to human eyes, such as air bubbles in shear images. Specifically, it uses sliding-window-based hybrid convolutional neural networks that provide the input shear images into regions to identify bubble defects and then use faster networks to identify bubble defects in the tire shear images themselves, minimizing false alarm rates of up to 18% [2]. The principles of digital shearography are explained in [2; 3; 4]. To date, the digital shearography process has relied on detecting bubbles that are not visible to the naked eye, depending on the experience of the field operator's staff, which can be inconsistent. In addition, emerging new defect types may be missed or misdiagnosed by AI. This makes existing models for maintaining tire quality inadequate and requires work to develop and improve them. Among others, a defect (bubble) detection method based on an incremental YOLO architecture was developed for a tire manufacturer in Taiwan. In it, defects are classified into six categories, tire images are pre-processed to improve the visibility of less noticeable defects, and the amount of pre-training model learning data used has been increased. In addition, the incremental YOLO method uses small model training for previously unobserved defects to improve the model detection rate, enabling a detection accuracy of 98% and a sensitivity of 90% [5]. The loss of information about the original depth data can result in reduced error detection in tires. Anomaly detection in a depth image of a tire with a 16-bit size expressed in grayscale is of limited use compared to a three-channel image (colors, better represented shapes, higher brightness). Additionally, for tires, the same defects have different sizes and shapes. Improving the tire defect identification process required a four-step process in the form of image input, highlight image generation, image stacking, and image learning. This deep learning segmentation allows the detection of abnormal defects. Comparison of the pixel area with the actual error in the depth image and the pixel area of the error inferred by the DL network showed a 7-10% improvement in error detection with an 80% reduction in time [6]. This approach is  
Figure 1: Traditional tire manufacturing process (own figure based on [1; 2; 3]). in line with the general trends, as two main directions of transport development have been observed: the development of road infrastructure (more durable materials and improved techniques for laying and repairing pavements) and vehicles (greater steerability, control, and safety).