## 5 Results and Discussion  
This section presents the results obtained using the LBP, HOG, and saliency methods for feature extraction, which were subsequently used as input to the CNN classifier. Each feature extraction method captured specific aspects of the images to prepare the feature vectors to be fed into the fully connected layer (FCL) of the CNN classifier. The study was organized into two main parts of general tests, as shown in the diagram in Figure 14. In each part, experiments were conducted both with and without data augmentation, evaluating four different strategies: CNN, CNN with HOG, CNN with LBP, and CNN with saliency. Table 2 shows the execution times for the different methods used in this study, focusing on the training time, test time, and image-processing time (IP Time). The table includes the results for the CNN model and the feature extraction filters (HOG, LBP, and SAL), applied both with and without a color filter (CF) and data augmentation (DA). The CNN without a color filter (CF) exhibited the highest training time, ranging from 2.4554 s to 5.0314 s. This is likely because the model handled four classes, whereas only two classes were used when a color filter (CF) was applied. When the color filter was used, the training time was reduced, with a maximum of 3.4395 s and a minimum of 2.4554 s. Models combining the HOG and LBP filters exhibited shorter training times. The HOG  
Figure 14: Analysis of general test results. CF: color filter; DA: data augmentation. filter had a maximum training time of 1.7918 s and a minimum training time of 0.8802 s, while the training times for the LBP filter ranged from 1.0426 s to 2.6672 s. In contrast, the SAL filter exhibited training times from 1.3243 s to 1.4202 s. Regarding the testing time, the CNN without a color filter exhibited times ranging from 0.1307 s to 0.1532 s. When using the HOG and LBP filters, the times were significantly reduced, ranging from 0.0722 s to 0.0874 s for HOG, and from 0.0797 s to 0.1202 s for LBP. The SAL filter also achieved faster testing times, with values ranging from 0.0945 s to 0.1004 s. The LBP filter exhibited the highest image-processing time, with values ranging from 0.209 s to 0.248 s. In comparison, the HOG and SAL filters required less time, with HOG ranging from 0.054 s to 0.108 s, and the SAL filter ranging from 0.519 s to 0.539 s.  
Tables 3 and 4 illustrate the confusion matrices, one with a color filter and the other without a color filter, both with data augmentation. In these examples, we utilized a convolutional neural network (CNN) trained on augmented images generated by applying the saliency algorithm for data extraction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **C1** & **C2** & **C3** & **C4** \\ \hline C1 & 67 (98.53\%) & 0 (0\%) & 1 (1.47\%) & 0 (0\%) \\ C2 & 0 (0\%) & 79 (95.18\%) & 0 (0\%) & 4 (4.82\%) \\ C3 & 1 (1.39\%) & 0 (0\%) & 71 (98.61\%) & 0 (0\%) \\ C4 & 0 (0\%) & 2 (2.99\%) & 0 (0\%) & 65 (97.01\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix with a color filter and with data augmentation. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Method** & **Condition** & **DA** & **Training Time (s)** & **Test Time (s)** & **IP Time (s)** \\ \hline CNN & No CF & No DA & 5.031 & 0.153 & - \\ CNN & No CF & With DA & 2.552 & 0.130 & - \\ CNN & With CF & No DA & 3.439 & 0.162 & - \\ CNN & With CF & With DA & 2.455 & 0.166 & - \\ HOG & No CF & No DA & 1.708 & 0.078 & 0.088 \\ HOG & No CF & With DA & 0.880 & 0.072 & 0.108 \\ HOG & With CF & No DA & 1.791 & 0.077 & 0.054 \\ HOG & With CF & With DA & 1.290 & 0.087 & 0.081 \\ LBP & No CF & No DA & 1.042 & 0.079 & 0.248 \\ LBP & No CF & With DA & 1.957 & 0.120 & 0.228 \\ LBP & With CF & No DA & 1.800 & 0.094 & 0.209 \\ LBP & With CF & With DA & 2.667 & 0.094 & 0.205 \\ SAL & No CF & No DA & 1.420 & 0.094 & 0.519 \\ SAL & No CF & With DA & 1.324 & 0.100 & 0.539 \\ SAL & With CF & No DA & 1.178 & 0.119 & 0.517 \\ SAL & With CF & With DA & 1.594 & 0.134 & 0.511 \\ \hline \hline \end{tabular}
\end{table}