## 3 Materials and Methods
### Data Pre-Processing and Filtering  
The approach presented here is a multi-class classification problem, in which we take the eight classes of the proposed dataset and train an optimized object detector model (MobileNet, RetinaNet, but it could be others) to implement a road damage acquisition system. However, training deep learning-based object detectors is not a trivial task; one of the major issues of deep learning, when working with relatively small datasets such as the one used in this study, is the problem of overfitting. Data augmentation (DA) is a manner for \"synthetically\" generating novel training data from the existing examples in a dataset. They are generated by submitting images in the latter to several image processing operations that yield believable looking images. The process of DA can significantly reduce the validation loss, as shown in Figure 6, where the plots for the training and validation for two identical ConvNets are shown--one without using data augmentation and the other using the strategy. Using data augmentation clearly combats the overfitting problem, the effect of which can be observed in the left side of the figure (the validation loss decreases and then increases again, while the training loss decreases for the training set). The right side of the figure shows how the validation loss can be regularized using data augmentation, among other strategies. The data augmentation approach has proven to be a successful method in several computer vision applications, but there are some major considerations when applying these transformations for tasks such as image classification and object detection. The main issue is related to the potential poor quality of many of the training examples, which can lead to suboptimal models. Such models could introduce a great number of false positives or, even worse, misclassify a new training example, as thoroughly documented by the authors in [27]. The authors of that study carried out a battery of tests in which they evaluated the performances of some well-known ConvNets (i.e., VGG16 and Google Net, among others) using images corrupted with different kinds of image quality distortions: compression, low contrast, blurring, and Gaussian noise. The authors found that the networks are resilient to compression distortions, but are greatly affected by the other three. Figure 6: Train and validation loss for two identical DNNs, (**a**) without using data augmentation and (**b**) with data augmentation. The loss clearly shows how data augmentation (DA) prevents overfitting. These findings have led to a great deal of research in this domain, with some works concentrating on the effect of such image quality distortions for several computer vision tasks based on deep neural nets [29]. To the best of our knowledge, no image quality assessments have been reported by previous works with the road damages datasets, which typically include a great deal of poor-quality images, as depicted in Figure 7. Some of the images have very low contrast, while others present blurring or an overall lack of sharpness, and others suffer extreme saturation. Therefore, in addition to the data augmentation strategy outlined above, the second contribution of this paper is the implementation of an image quality assessment to determine if a training example can be considered a good candidate for the data augmentation phase and for further processing (i.e., for the ConvNet training process). Some of the considerations for evaluating the image quality, as well as some of the image preprocessing algorithms to determine it, will be discussed as follows. As mentioned above, the performance of the ConvNet models can be severely affected by the quality of the input images; the authors in [26] performed individual tests on for Gaussian noise, blur, compression, and low contrast.