## 3 Experiments and Results
### Classification of Pathologies in Concrete  
YOLO can classify even with detection, but we experimented with its classification power in isolation to show the possibility of improving detection or having images focused on just one pathology.  
This time, the learning process for classifying images with and without concrete pathologies was carried out using YOLO version 8. The reference paper used training in a customized neural network for classification.  
The dataset used was the same as in the previous experiment by [PERSON] [17]. This dataset had a significant number of images, 40,000, balanced and annotated for cases with and without cracks in the concrete.  
The dataset was divided into training, validation, and testing, with 70% for training, 20% for validation, and 10% for testing. This division allows adequate training for learning the neural network, but the ultimate goal is to use the images cropped in the detection to be classified by this trained network. This final step on the cropped images allows the identification of false positives resulting from detection.  
Figure 8 shows the Loss curve during training. The training converges quickly to low-loss values but keeps oscillating at low values, and using the early stop feature the lowest value is reached at epoch 306, corresponding to an accuracy of 99.65%.  
Figure 6: Positive detection [13].  
Figure 7: Crack by author.  
Figure 9 shows the Confusion Matrix applied to the test set. For classification, the trained neural network could fully identify the true positive cases, with only part of the negative cases not being adequately identified.  
Table 1 shows the accuracy achieved in both the latest work carried out for this article and the previous work from the previous article [4]. The current work was carried out with YOLO, version 8, and achieved an accuracy of 99.65%, an improvement on the previous work, which used a customized neural network and achieved 99.43% [4]. The results are also compared with other works found in the literature regarding the parameters of how good the results were. Recall was 99.925% and F1 score was 99.787%.  
The trained neural network, YOLO version 8, allows pathologies to be detected. This identification is done by restricting a bounding box around the pathology identified.  
It is possible to crop out the pathology identified in the detection in the bounding box. This cropping then generates a dataset that can be used for classification. The  
Figure 8: Loss with YOLO, version 8.  
Figure 9: Confusion Matrix with YOLO, version 8.  
cropped images result from detection, so they are annotated with the type of pathology they represent.  
The test dataset was used to detect and crop concrete cracks for the classification task. The thickness of the bounding box and the identification label were minimized to obtain a cleaner image for classification.  
Detecting cracks in the concrete generated a set of images classified with this pathology. For the reasons already explained, these images contain false positives, images classified as a concrete cracking pathology but which do not have this pathology.  
Since another YOLO version 8 neural network has been trained for classification with a dataset containing many images, it can be used to identify false positives. The neural network for classification was then used to reclassify the images generated by detecting concrete crack pathologies, Figure 10. This pathology was used because the dataset with significant images was of this pathology. However, this same procedure can be used for other pathologies if a dataset is available for this activity.