## 3 Experiments and Results
### Detection of Pathologies in Concrete  
The paper referred to as the basis for this current paper presented training for detection using YOLO version 4. This paper presents the same training for detecting pathologies in concrete but uses a more recent version of YOLO, in this case, YOLO version 8. The same training database, the dataset, is used to compare and evaluate gains with a new version of the neural network architecture. The training times evaluated are the same for both experiments, making it possible to evaluate the final accuracy and how the training evolved. The two training sessions were measured up to the same epoch, but more epochs can be trained to improve the final accuracy. Learning is directly related to the quality of the dataset annotation. An annotation with errors results in poor learning. The neural network's ability to detect a given object will depend on what it has been taught; if we teach it the wrong object, the neural network will reflect this. For example, if we present the pathology of corrosion instead of cracking, corrosion will be detected instead of cracking. The detection experiment carried out with the CODEBRIM [13] database enables the identification of specific pathologies in more comprehensive images. The image was not cropped specifically for the pathology to be classified, as is necessary in the case of classification. The dataset [13] has 1052 images. The set of images presents defects for the following classes: 2507 cracks, 1898 spallation, 833 efflorescence, 1507 exposed bars, and 1559 corrosion stains [16]. Figure 3 shows the cases of fissures or cracks in A, exposed steel bars in B, spallation in C, efflorescence in D, and corrosion stains in E.  
A review of the dataset CODEBRIM [13] was conducted to detect possible inconsistencies in the annotations for the concrete crack pathology. Some figures were completely wrong in relation to the positioning of the bounding box; these cases were possibly due to the rotation of the image after annotation. Another improvement was the better use of crack pathologies that were present in the images but had not been annotated. Figure 4 shows a case of an incorrect annotation due to the image's rotation. The marked points that define the bounding boxes are not correctly on the pathologies, due to image rotation. Figure 2: YOLO architecture detector [15]. The review of concrete crack annotations was performed in an environment created in Anaconda. The resource used was LabellImg, which was installed in the created environment via a prompt command. LabellImg allows one to view the annotations created, correct existing annotations, and even create new ones. The most incorrect annotations were deleted, and new ones were created. Annotations with minor inaccuracies were adjusted. New annotations were created where there was pathology without an annotation. Figure 4: Wrong annotation due to image rotation [13]. Figure 3: Pathologies in concrete [13]: (**A**) Crack, (**B**) Exposed Bar, (**C**) Spallation, (**D**) Efflorescence and (**E**) Corrosion Stain. Additional training was carried out with the revised base so that the results could be compared with the original base to measure the gain from the revised annotation. This training with the revised base was performed using YOLO version 8 since the training with the original base was also performed using this version. The use of the same version of YOLO under the same conditions, except for the fact that the original base and the revised base are used, allows us to understand the impact of the dataset revision. It is important to note that the number of wrong annotations was small in relation to the total universe of images, so the second training session was not exposed to such significant variations in learning quality. The sequence of results with YOLO for detection was then 0.12 mAP, YOLO version 4, with the original dataset; 0.13 mAP, YOLO version 4, with the revised dataset; and 0.14 mAP, YOLO version 8, with the revised dataset.