## 2 Related Works  
The automation of any task using computer vision depends on the use of robust real-time computational methods with a great level of intelligence. However, in the context of road damage inspection systems, most of the initial approaches were originally devised as a mechanism for aiding the road inspectors as simple acquisition instruments using image processing techniques, whereas other systems relied upon heavily instrumented MMSs. Both approaches were rarely real-time and were constrained by capabilities of the machine learning algorithms of the time. For a more comprehensive survey of the various acquisition techniques, there exist excellent studies in 3D imaging [8], remote sensing [9], and computer vision and image processing in general [10,11]. It has been recognized for some time that computer vision (CV) approaches were among the best technological choices for implementing asset management tools, but several technical limitations hampered their widespread adoption. In particular, the biggest hurdle for automating asphalt damage detection and classification is to consistently achieve high classification accuracies (and high recalls) for a typically large number of classes, in very challenging environmental conditions (i.e., wide changes in illumination and camera pose, weather conditions, among others). Despite these issues, various  
Figure 1: Some samples of road damages tackled by road inspection systems and agencies: (**a**,**b**) linear cracks, (**c**) peeling, (**d**) alligator cracks, (**e**) patches, and (**f**) potholes. computer vision-based methods for automatically classifying individual structural damages can be found in the literature. This lack of more comprehensive approaches stemmed from the shortage of large road damage datasets (although the Kitti [4] dataset has been used by some researchers). This problem has been addressed in recent years by academics working in the domain, although it remains an open problem, as it is common to find unbalanced classes for some types of structural damages. Regardless of the aforementioned problems, these datasets were deployed by many studies to implement and test artificial intelligence-based methods, using conventional image features and machine learning models such as support vector machines for the identification of cracks [12] and potholes [13]. As larger sets of images became more common, DL-based methods to address the classification of road damages have become the norm in the field. It is widely recognized [14] that algorithms based on such architectures have obtained outstanding results in several image analysis and classification problems. In this sense, several studies have been carried out that aim to take advantage of the capabilities of neural networks' architectures for improving the detection and classification of road asphalt damages, attaining very competitive results for certain classes such as asphalt cracks [15; 16]. An additional benefit of using such CV-based methods over other approaches (i.e., MMS) is that cheaper imaging devices can be used for acquiring the samples. Technologies based on mobile devices were explored previously, which made use of traditional algorithms such as Haar-cascade classifiers [17; 18], but these works did not make comparisons with DL-based acquisition systems. Despite the good results of these approaches, they suffer from the fact that custom datasets were employed and the results are not scalable or easily replicable, as the tool was intended for \"static analyses\" (simple image capture and classification). For instance, Figure 2 shows examples of image acquisition of road damage instances using an LPB-based approach on OpenCV. Conversely, recent strides in deep learning techniques have enabled the deployment of very efficient and resource-constrained object detection systems in mobile devices [19; 20] and have started to be deployed on road damage acquisition systems [21; 22].