## 5 Conclusions  
The methodology presented in this article makes it possible to detect and classify pathologies in concrete structures using deep neural networks using computer vision with artificial intelligence. The proposal enables robust results using deep learning, showing the gains the field of computer science can make possible to enhance civil construction. One thing that is fundamental for using the methodology proposed in this article is the existence of annotated datasets to identify specific pathologies. The advancement of artificial intelligence has favored the emergence of many databases for this area, as has occurred in several other areas. These datasets enable several works similar to the one presented in this article. The techniques used in this area have also significantly advanced, resulting in greater precision, agility, and ease of use for identifying pathologies. Studies with these encourage the appearance of new work and the advancement of the resources used for this fundamental task of civil construction. The classification accuracy was 99.65%, which allows adequate performance for the classification task. The detection task on the test base adequately identified pathologies in images of concrete structures. This performance reinforces the idea that using neural networks provides an essential gain for analyzing pathologies, carried out as a measure to maintain concrete structures. There are specific cases in which the identification of pathologies is more difficult, even with a usual approach, and cases in which the pathology is not adequately highlighted in the image used. For example, \"corrosion spots\" and/or \"efflorescence\" may overlap or even, due to the quality of the image, be very similar, making it challenging to define a reasonable classification in this case. Another case, as an example, is when a crack is confused with a joint of certain parts of the structure; this is because the crack may have a straight geometric shape or it could be a case in which the junction of two parts suffered an unwanted separation. Although the performance of neural networks can be highly accurate, they cannot guarantee complete freedom from error, and some detections and consequent classifications result in false positives, an indication of pathology that is not a case of pathology in concrete. These specific isolated cases can be solved using a second neural network specifically for classification, trained under better conditions, to provide much greater prediction and increase the chances of eliminating these inconsistencies. The training for learning the neural network for detection and the learning of the neural network for classification may be performed differently depending on the dataset used. It is easier to annotate for classification, which may justify the easier availability of datasets with more images and better annotations. In the practical examples in this article, the dataset used for classification has 40,000 images, while the one used for detection has only 1052 images. As a result, the second network trained for classification will have a greater ability to avoid false positives. This is the advantage of using two datasets if available and if the second dataset, specific for classification, is better quality. Not only is the quantity of images relevant, but so is the quality of the annotations. Errors in the annotations compromise the learning of the neural network. In the case of annotations for detection, annotations are more challenging. They can depend on subjectivity, even if carried out by a qualified professional, although professional skill lends greater credibility to the annotations. This article benefited from a review of the dataset used for detection, albeit for only one of the pathologies, since the work was focused on concrete crack pathology. A review is something that should be part of the stage of preparing the dataset to be used. Computer vision techniques have increasingly become part of human daily life in the most diverse areas that can be analyzed.