## 4 Discussion  
Experiments were carried out to verify that the proposed methodology is valid. The proposal described in this article can also use a previous experiment and apply the proposed method to assess whether the objective of reducing false positives can be achieved. Two actions are applied in the experiment to improve the results: The first consists of updating the technology used, from version 4 of YOLO, initially used in the first experiment, to version 8, which consists of the evolution of the previous technology. The second consists of reviewing the dataset used, CODEBRIM, in the first experiment to identify possible inconsistencies and correct them to allow the neural network to learn better. The classification dataset was also trained with YOLO version 8 instead of the customized network used in the first experiment. Regarding this last point, the idea of the first \begin{table}
\begin{tabular}{l c} \hline \hline
**Author** & **Accuracy (\%)** \\ \hline Previous paper & 99.43 \\ Customized CNN [4] & 99.65 \\ \hline This paper & 99.65 \\ YOLO v8 & 98.6 \\ \hline [PERSON] [18] & 95.92 \\ \hline [PERSON] [20] & 98.6 \\ \hline [PERSON] [21] & 99.98 \\ \hline [PERSON] [22] & 98 \\ \hline [PERSON] [23] & 99 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: YOLO classification. Figure 10: YOLO version 8 crack classification [13]: (**A**) Positive for crack and (**B**) Negative for crack. experiment is maintained to make it possible to use a specific dataset containing more images to identify false positives from detection. The dataset uses the same training database to compare and evaluate gains with a new version of the neural network architecture. The training assessed times are the same for both experiments, making it possible to evaluate the final accuracy and how the training has evolved. The two training sessions were measured up to the same epoch, but more epochs can be trained to improve the final accuracy. A complete dataset review was conducted to detect inconsistencies in the annotations for the concrete cracking pathology. Some identifiers were utterly wrong concerning 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 in images that had not been annotated. The learning of a neural network is directly related to the quality of the dataset annotation--faulty annotation results in poor learning. The neural network's ability to detect a particular object will depend on what it has been taught; if we teach the wrong object, the neural network will reflect this. For example, if we present the pathology corrosion instead of cracking, corrosion will be detected instead of cracking. The corrections made, therefore, represent important actions to improve learning. The task of detection, locating an artifact in a more general image, is more complex than simple classification. Classification is usually less complex, as it simply consists of classifying the entire image to be processed; detection, on the other hand, has this same activity but on a section of the image that needs to be located, thus requiring more processing. Classification and detection annotations are also different. The classification should be less complex, as it is enough to inform the classification class to which an image belongs. In the case of detection, it is necessary to locate the artifact to be classified within the image to produce the marking, and the number of artifacts in the same image can be numerous. The marking of artifacts in the annotation for detection can be subjective, so a qualified professional must carry it out.This can be difficult in many cases, depending on how the artifact appears in the image. These latter difficulties can result in errors or inaccuracies in the annotations. The greater degree of difficulty in annotations for detection makes this task more time-consuming and expensive, which can lead to fewer datasets being available.