# A Method for Detecting Pathologies in Concrete Structures Using Deep Neural Networks
## 4 Discussion  
This paper presents a method for pathology detection and classification in concrete structures using deep neural networks. The method showed the possibility of speeding up the acquisition of results and obtaining more robust results, using deep learning knowledge for an area that Computer Science can enhance, Civil Construction. Some datasets already enable the development of studies with concrete structures, showing that similar studies are already developing in this area. The results presented in this paper confirmed the possibility of gains in the studied area and showed that it is possible to develop resources with the existing material and techniques. Papers such as this one may stimulate the emergence of more datasets and, consequently, new applications. The classification results were analyzed satisfactorily, with an accuracy and precision of 99.43%. The graphs show a convergence to good results, training, and validation, both in terms of accuracy and loss, which, in the loss, ends with acceptable low values. The confusion matrix also collaborates with the analysis to conclude that the neural network is suitable for identifying concrete pathologies. It is possible to observe in the detection result that the method presents a good ability to detect pathologies in concrete, with individualized image testing in good conditions, but allows the questioning about improving accuracy. The trained network identified the proposed pathologies, but the accuracy needs to be better evaluated with more training epochs so that it can be concluded whether the obtained accuracy can increase with training for more epochs. There are cases in which pathology detection, and consequent classification, can be difficult or even impossible because it is not well evidenced in the image. This is the case of stains that may overlap with the cases of \"corrosion stains\" and/or \"efflorescence\". Some cases of cracking, which have a straight geometric shape, can also be confused with structural joints or splits. Since the detection system is not 100%, the processing may result in false positive regions, regions that have been indicated as containing pathology but do not have pathology. By associating the expert classification system with the sequence, it is possible to identify  
Figure 13: Classification result for two images from the two classes of the [18] dataset. these false positives. These regions do not have pathology and thus correct the false positive by disregarding it. The qualifying practice sessions were better than the detections. Since detection involves the classification task, the difference in results must be investigated and tested under different conditions. The dataset used for the classification has 40,000 images. The dataset used for detection has only 1052 images. The difference is considerable, even considering that the images used for detection may have more than one pathology annotation per image, even considering a single pathology type. As a suggestion, the dataset used for detection can be further investigated to identify a bounding box that has been poorly annotated. It is even possible to give more weight to annotations made by technical experts if necessary to increase the reliability of the analyses. One point to consider for the detection result is the analysis of the annotated bounding boxes in the dataset and how well-fitted they are. The annotation work must be performed by a professional who has the ability to identify the pathologies, and, being a tedious and human activity, it may contain imperfections that impact the results. Nevertheless, on the difference in performance between classification and detection, a point to analyze is the possibility of using the training performed in classification to improve the detection results since detection also has a classification step. One option is to use the classification training performed with a CNN to reduce the false positives obtained. CNN represents an essential solution for diagnostic imaging cases [26].