# Approach for Tattoo Detection and Identification Based on YOLOv5 and Similarity Distance
## 4 The Experimental Investigation
### The Experiments Performed on the Influence of Data Augmentation Parameters  
As mentioned, the results of tattoo detection can depend on various parameters. First of all, the influence of data augmentation parameters has been analyzed using the pre-trained YOLOv5l model, as according to the related work analysis performed, for such types of tasks, it is the most suitable model. The results of the experiment are presented in the table below (Table 3). All augmentation variations were trained on the deMSI dataset with 300 epochs. On the basis of the provided experimental results, some considerations were made. Crop augmentation allowed us to obtain the highest [EMAIL_ADDRESS] (0.82) and good precision and recall scores. It would be an effective option in this study. When talking about balanced precision and recall, hue augmentation showed a good balance between precision and recall, with a decent [EMAIL_ADDRESS] score (0.794). So, this augmentation should also be considered. In addition, computational efficiency must be considered: flip, 90-degree rotation, and blur. These enhancements showed a good balance between precision and recall with moderate [EMAIL_ADDRESS] scores. They may be computationally efficient. Also, it should be mentioned that it is a good practice to avoid low-performing augmentations; in this case, it would be grayscale and rotation. These augmentations had lower [EMAIL_ADDRESS] scores. Depending on priorities, it might be good to consider excluding them from the final augmentation strategy. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Augmentation** & **Augmentation Specifications** & **Precision** & **Recall** & **mAP@ 0.5** & **[EMAIL_ADDRESS]:0.95** \\ \hline No augmentation applied & 0.735 & 0.733 & 0.739 & 0.454 \\ \hline Flip & Horizontal and vertical & 0.819 & 0.748 & 0.799 & 0.582 \\ \hline
90\({}^{\circ}\) Rotate & Clockwise, counterclockwise, and upside down & 0.834 & 0.726 & 0.801 & 0.608 \\ \hline Crop & 0\% minimum zoom and & & & & \\  & 30\% maximum zoom & & & & \\ \hline Rotation & Between \(-\)15\({}^{\circ}\) and +15\({}^{\circ}\) & 0.861 & 0.741 & 0.788 & 0.51 \\ \hline Shear & \(\pm\)15\({}^{\circ}\) horizontal and \(\pm\)15\({}^{\circ}\) vertical & 0.811 & 0.733 & 0.794 & 0.548 \\ \hline Grayscale & Applied to 25\% of images & 0.788 & 0.674 & 0.735 & 0.46 \\ \hline Hue & Between \(-\)25\({}^{\circ}\) and +25\({}^{\circ}\) & 0.835 & 0.8 & 0.794 & 0.568 \\ \hline Saturation & Between \(-\)30\% and +30\% & 0.818 & 0.756 & 0.804 & 0.583 \\ \hline Brightness & Between \(-\)25\% and +25\% & 0.849 & 0.689 & 0.77 & 0.543 \\ \hline Exposure & Between \(-\)15\% and +15\% & 0.816 & 0.723 & 0.786 & 0.551 \\ \hline Blur & Up to 2 px & 0.814 & 0.756 & 0.8 & 0.566 \\ \hline Noise & Up to 1.49\% of pixels & 0.78 & 0.763 & 0.78 & 0.572 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Augmentation experiment results. To achieve even higher results, it was decided to try to combine multiple augmentations (Table 4). Taking into account the metrics [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 metrics, group 4 (90\({}^{\circ}\) rotation, shear, and blur) appears to have the best overall performance, with the highest values of [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 values. Therefore, group 4 was chosen as the best-performing augmentation strategy. Based on the results of the experiments provided, the following pre-processing and augmentation steps were applied in this study. * Resize. The images were resized to a uniform dimension of 320 \(\times\) 320 pixels, which is a common choice for the YOLOv5 models. This step not only standardizes the input size but also enhances training efficiency;
* 90\({}^{\circ}\) Rotate. The images were rotated 90 degrees during the augmentation process. This rotation can help the model become more robust to object orientations in the training data. It introduces variations in the orientation of objects, making the model more versatile;
* Shear. Shearing involves shifting one part of an image in a certain direction, creating a \"tilted\" effect.