## 1 Introduction  
The global rise in the human population increases the need for various modes of transportation, including automobiles, buses, and trucks. This particular circumstance pushes numerous manufacturing facilities to play a role in the production of a crucial component for automobiles, namely, the tire. However, it is important to note that the annual return rate of defective tires is at 7 percent of the total tire production size, leading to a yearly restitution amounting to USD 100 million [1]. To minimize the number of tire returns, it is necessary to implement quality-inspection procedures that involve the utilization of X-ray imaging for the detection of defects in tires. Non-destructive testing (NDT) methods, such as radiographic X-ray testing, have been instrumental in identifying latent defects in tires. Following tire production, each tire undergoes an X-ray inspection to create images that facilitate rapid and reliable interpretation. These images are then evaluated by quality control operators, who categorize tires as non-defective or defective. In Figure 1, examples of a non-defective (perfect) tire obtained from the X-ray device are presented. While some defects are identifiable through computer-aided demarcation lines, detecting foreign objects with the naked eye proves challenging. Such objects often vary in size and properties, making detection even more arduous, particularly during periods of operator fatigue. Furthermore, the process is inherently subjective and characterized by inefficiency, time consumption, and potential bias, necessitating a significant degree of concentrated effort [2]. With the development of technology, artificial intelligence has been used in autonomous vehicles, quality control processes, and many similar areas of car technology [3]. In recent years, the field of deep learning (DL) has undergone significant advancements that have led to innovative solutions for numerous industrial challenges, including tire defect detection problems. DL techniques aim to categorize images by extracting meaningful features from the images. In many datasets, especially datasets taken from industrial applications, it is difficult to obtain sufficient labeled data from each class. In such cases, pre-trained transfer learning (TL) models can enhance performance with less labeled data by benefiting from the experiences gained during the training phase. Thus, rather than training a model from scratch, the model previously trained on a comprehensive dataset using TL methods could be applied to another dataset. AlexNet, VGG, ResNet and Xception, DenseNet and their variations, and TL approaches are some of the most frequently used methods [4]. TL techniques can be applied in two ways. First, the TL model can use the weights obtained with the ImageNet dataset to extract features. It learns general data features and patterns from this dataset. These features can be used because they provide better generalization across different but similar tasks. Secondly, it is fine-tuned with a new dataset and transformed into a model suitable for that dataset [5]. In the examination of models in the literature, the Xception model demonstrated better accuracy in the classification task with the ImageNet dataset, with fewer parameters than many other deep learning models [4]. While DL has demonstrated remarkable success in various real-world applications [6; 7], its inherent stochastic nature can undermine trust in its outcomes. To enhance reliability, there is a critical need to elucidate DL models' decisions, ensuring transparency and fostering trust in their results. Thus, the concept of Explainable Artificial Intelligence (XAI) has become increasingly important in the domain of DL [8; 9; 10; 11; 12]. XAI seeks to enhance the transparency and reliability of the results produced by artificial intelligence (AI) systems by offering visual explanations in the form of heatmaps generated through techniques such as Grad-CAM [13].