## 2 Related Works  
The adoption of machine learning methods for automatic tire defect detection has gained popularity, with various algorithms and approaches explored. A study conducted by us [1] focused on tire classification using basic machine learning classifiers such as the SVM (Support Vector Machine), and kNN (k-Nearest Neighbors), ANN (Artificial Neural Network), and texture features, achieving approximately 99% accuracy.  
While limited, the literature contains some notable studies concentrating solely on foreign object detection. For instance, the authors of [2] proposed a wavelet multiscale representation method to detect the defect of the tires from 400 radiographic images. They achieved 96.9% detection accuracy with the selection of the optimal scale and threshold parameters of defect edge detection.  
In [14], an end-to-end tire defect detection method was proposed with the combination of an optimized Semantic Segmentation Network and a compact CNN classifier. They used 3234 test images with four different defect types and achieve 96.5% classification accuracy. The authors used texture segmentation based on the Gabor filter and fuzzy c-means clustering to decrease the computation complexity.  
In Ref. [15], the authors proposed a TireNet model by using the Siamese network with some modifications. They preprocessed images as object and nonobject images to improve learning models. The classification model is generated by a Siamese network and weighted cross-entropy loss. The final model is fine-tuned with a balanced threshold. The experimental results were obtained for a total of 120,000 images with 20,000 defective tire images.  
In Ref. [16], an unsupervised approach is proposed to avoid labeling the defective images. The authors augmented the images with a Generative Adversarial Network (GAN)-based method. The features of the non-defective tire images were extracted, and these features were used for image reconstruction. The network memorized these features, and when a defective image was input into the generator, the difference between the features could be detected. This method achieved 79.8% accuracy with a training set of 20,000 defect-free images and a test set of 2077 defective and 3947 defect-free images.  
The authors of [17] proposed a Faster RCNN network based on the feature pyramid network to detect tire bubble defects. Their model contains four parts: the backbone, Tire Feature Pyramid Network, region proposal, and region prediction. Experimental results were evaluated with mean average precision and average precision. The model obtains better results than the classical feature pyramid network.  
In Ref. [18] a class-level, weighted, partial domain adaptation network-based defect detection method is proposed for five different defect types (namely, impurity, bubble, slack, bend, and overlap). An average accuracy of 95.07% was obtained with the defective tire dataset. The same authors present a Transferable Swin Transformer-based method [19] for tire defect detection under domain shift conditions. The Swin Transformer is used as the feature extractor for all the images. This model exhibits 96.17% average accuracy.  
In Ref. [20] a pixel-level defect detection method based on transformers is proposed. This method detects the type of defect as well as the geometric shape of the defect. The experimental results were obtained with 1450 tire images that contain the six types of defects: Tread and Sidewall Foreign Matter, Tread and Sidewall Core Cracking, Sidewall Core Overlapping, and Sidewall Bubbles.  
In another study [21], the authors augmented an imbalanced dataset with the Wasserstein Generative Adversarial Network (WGAN). The new balanced dataset was classified with transfer learning methods, and the best classification accuracy obtained with the ResNet model was 95.92%.