## 2 Related Work  
During the last few years, many datasets of road damages were released and made publicly available; some of these datasets are being used extensively by other researchers, such as the RDD2018 dataset released by [PERSON] et al. [4], which contained around 9000 images of eight types of road damage in Japan. The RDD2020 by [PERSON] et al. [5] included around 26,000 images of eight classes of damage collected from the roads of India, Japan, and the Czech Republic. In general, road damage datasets can be divided into two types based on their uses. Detection datasets that are used primarily for binary classification such as the AigleRN dataset collected from France by [PERSON] et al. [6], CFD collected from China released by [PERSON] et al. [7], Temple University dataset collected from the United States by [PERSON] et al. [8], Denish Technological Institute dataset collected from Denmark by [PERSON] et al. [9], and METU gathered from Turkey released by [PERSON] [10]. The other type is the classification datasets which usually contain more than two positive classes, such as the Taiwan and Japan datasets released by [PERSON] et al. [11], the Czech and Slovak datasets released by [PERSON] et al. [12], and many others. Machine learning (ML) algorithms have been used for more than two decades to detect road damage, such as the work completed by [PERSON] et al. [13], [PERSON] et al. [14], and [PERSON] et al. [15]. Numerous studies have been proposed to detect potholes and cracks using edge detection and image thresholding, for instance, [PERSON] et al. [16], [PERSON] et al. [17], and [PERSON] et al. [18]. Many studies developed methods to detect cracks utilizing random structured forests, e.g., [PERSON] et al. [7], and an unsupervised method based on [PERSON]'s thresholds and photo-metric information, e.g., [PERSON] et al. [19]. The ML methods used to detect potholes detection included unsupervised fuzzy c-means clustering and morphological reconstruction, e.g., [PERSON] et al. [20], and Support Vector Machine (SVM), e.g., [PERSON] et al. [21]. Furthermore, [PERSON] et al. [22] and [PERSON] et al. [23] implemented several ML classification methods to classify four types of road damage, including K-nearest neighbors (KNN), Bagged Trees, SVM, and Decision Tree. In recent years, deep learning (DL) has been widely used to detect road damage, for instance, [PERSON] et al. [24], [PERSON] et al. [25], and [PERSON] et al. [26]. These algorithms and techniques are being used now in self-driving cars to avoid obstacles and ensure road safety while driving. Most research uses detection tasks that only discover the damage. In many studies presented by [PERSON] et al. [8], [PERSON] and [PERSON] [9], [PERSON] et al. [27], and [PERSON] et al. [28], several Convolutional Neural Network (CNN) models were presented to detect cracks in road images. Despite the good results that were achieved, the detection method only determines the presence of the damage; still, it does not classify its type. As a result, in recent studies, classification methods based on DL algorithms have been used on input images to classify them into various types of damage that can assist municipalities in accurately identifying and classifying damage. For instance, [PERSON] et al. [29] and [PERSON] et al. [30] presented a method to detect four types of damage to the road using several CNN models. Despite the good results that were achieved in the previous studies, the method detects only four types of road damage. However, a number of methods were developed to classifiy more than six types of road damage using different techniques. [PERSON] et al. [31] and [PERSON] et al. [12] proposed a method for road damage detection and classification based on Convolutional Neural Networks (CNNs). The model was trained using the SSD MobileNet and SSD Inception V2 frameworks. [PERSON] et al. [32], [PERSON] et al. [33], [PERSON] et al. [34] and [PERSON] et al.