# People Detection Using Artificial Intelligence with Panchromatic Satellite Images
## 3 Results  
Table 6 shows the results of people detection without using data augmentation or a 15 cm buffer. The F1 score at 60 cm corresponds approximately to the AP when training the given model (31%). When using the mask, the number of TPs decreased and the number of FNs increased. There was also a lower number of FPs, caused by a smaller number of predicted people; therefore, the value of the F1 score reached only 28%. Table 7 shows the results obtained using a 15 cm buffer without data augmentation. When the buffer is used, an increase in TPs can be seen in both cases, which corresponds to a decrease in FNs. The F1 score for the model without a mask achieved an approximately 3% improvement compared to the model without a buffer. When using the mask, the model utilizing the buffer achieved an F1 score approximately 12% higher. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model type** & **Ratios** & **Backbone** & **Batch Size** & **Learning Rate** & **Zoom/ Scales** & **Grids** & **Dropout** \\ \hline SSD & [1.0, 1.0] & \begin{tabular}{c} VGG16, VGG19, \\ ResNet152, and \\ DenseNet201 \\ ResNet50, ResNet101, \\ and ResNet152 \\ \end{tabular} & \begin{tabular}{c} 2, 4, 8, 16, 32, \\ and 64 \\ 2, 4, 8, 16, 32, \\ and 64 \\ \end{tabular} & Define by lr\_find() function & [0.1–0.5] & 4,8,16,32 & 0.2 \\ Retina-Net & [1.0, 1.0] & \
\begin{tabular}{c} ResNet50, ResNet101, \\ and ResNet152 \\ \end{tabular} &
\begin{tabular}{c} 2, 4, 8, 16, 32, \\ and 64 \\ \end{tabular} & Define by lr\_find() function & [2–8] & NA & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Settings for the training of the SSD and RetinaNet models using data augmentation. Figure 6: Validation process. Table 8 shows the results when using data augmentation. Regardless of whether a mask is used, the F1 score is higher when data augmentation is employed. In the case of using a mask, it is possible to see an increase of more than 20% in the F1 score (F1 score reaches more than 51%) compared to when not using data augmentation. When using data augmentation, it was possible to detect more than 50% of GT within 60 cm. Table 9 shows the results obtained using data augmentation and a buffer of 15 cm. In this case, more than 60% of GT were detected. For the model not utilizing a mask, the value of the F1 score is smaller than in Table 8, which is due to a larger number of FPs. Meanwhile, in the case of employing a mask, the model with data augmentation reached more than 54%, which is approximately 3% higher than when not using a buffer. Figure 7 shows the output of people detection using the Faster R-CNN method. Red squares indicate real people (ground truth), while blue squares indicate people detected by the Faster R-CNN algorithm. The Faster R-CNN algorithm was able to detect people in this image with relatively high accuracy. It can be seen that in some cases the squares overlap completely and in others they overlap partially. Nevertheless, there is still room for improvement, especially in terms of the size of the area that the algorithm identifies as human. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Limit (cm)** & **TP** & **FP** & **FN** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{Faster R-CNN without mask} & 15 & 7 & 107 & 207 & 6.1 & 3.3 & 4.3 \\  & 30 & 33 & 81 & 181 & 28.9 & 15.4 & 20.1 \\  & 45 & 46 & 68 & 168 & 40.4 & 21.5 & 28 \\  & 60 & 51 & 63 & 163 & 44.7 & 23.8 & 31.1 \\ \hline \multirow{4}{*}{Faster R-CNN with mask} & 15 & 6 & 48 & 208 & 11.1 & 2.8 & 4.5 \\  & 30 & 16 & 38 & 198 & 29.6 & 7.5 & 11.9 \\ \cline{1-1}  & 45 & 30 & 24 & 184 & 55.6 & 14 & 22.4 \\ \cline{1-1}  & 60 & 38 & 16 & 176 & 70.4 & 17.8 & 28.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of people detection without data augmentation and without a buffer.