## 3 Results
A true negative is recorded when no detection is made, and no human is present in the scene. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training** & **Accuracy** & **Loss** & \
\begin{tabular}{c} **Validation** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Validation** \\ **Loss** \\ \end{tabular} \\ \hline Subset0\_reLU & 0.9874 & 0.0108 & 0.9670 & 0.0134 \\ \hline Subset1\_reLU & 0.9816 & 0.0126 & 0.9722 & 0.0137 \\ \hline Subset2\_reLU & 0.9890 & 0.0123 & 0.9771 & 0.0125 \\ \hline Subset3\_reLU & 0.9880 & 0.0119 & 0.9767 & 0.0121 \\ \hline Subset4\_reLU & 0.9764 & 0.0119 & 0.9705 & 0.0125 \\ \hline Subset0\_swish & 0.9670 & 0.0102 & 0.9458 & 0.0132 \\ \hline Subset1\_swish & 0.9657 & 0.0108 & 0.9409 & 0.0144 \\ \hline Subset2\_swish & 0.9461 & 0.0136 & 0.9431 & 0.0135 \\ \hline Subset3\_swish & 0.9457 & 0.0136 & 0.9410 & 0.0138 \\ \hline Subset4\_swish & 0.9445 & 0.0140 & 0.9421 & 0.0138 \\ \hline Subset0\_reLU\_class & 0.9930 & 0.0258 & 0.9885 & 0.0360 \\ \hline Subset1\_reLU\_class & 0.9905 & 0.0371 & 0.9825 & 0.0556 \\ \hline Subset2\_reLU\_class & 0.9925 & 0.0291 & 0.9889 & 0.0368 \\ \hline Subset3\_reLU\_class & 0.9918 & 0.0283 & 0.9887 & 0.0359 \\ \hline Subset4\_reLU\_class & 0.9920 & 0.0349 & 0.9876 & 0.0429 \\ \hline Subset0\_swish\_class & 0.9880 & 0.0400 & 0.9798 & 0.0624 \\ \hline Subset1\_swish\_class & 0.9895 & 0.0355 & 0.9795 & 0.0578 \\ \hline Subset2\_swish\_class & 0.9898 & 0.0354 & 0.9817 & 0.0533 \\ \hline Subset3\_swish\_class & 0.9915 & 0.0345 & 0.9828 & 0.0569 \\ \hline Subset4\_swish\_class & 0.9903 & 0.0351 & 0.9813 & 0.0553 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Training results. Since the algorithm performs both object presence detection and localization, a true positive is counted when a human is correctly detected, and the predicted bounding box overlaps with the ground truth by at least 50% Intersection over Union (IoU). A false negative occurs when a human is present in the frame but either no detection is made or the bounding box fails to meet the IoU threshold. A false positive is defined as a detection in which no human is present or where the predicted bounding box does not correspond to any valid ground truth object. A true negative is recorded when no detection is made and no human is present in the scene. Although mean Average Precision (mAP) is a widely adopted metric in object detection benchmarks--particularly those involving multi-class tasks or dense object distributions--we use confusion matrix-based metrics due to the binary, single-class nature of our task. The algorithm is specifically designed to detect and localize humans in drone-captured video. Thus, our evaluation focuses on both the detection correctness and the localization precision of a single object class. In this context, accuracy, precision, recall, and F1 score provide more interpretable and practically meaningful insights into the model's real-world performance. Nonetheless, we acknowledge the comprehensiveness of mAP as an evaluation metric and plan to incorporate it in future work, particularly if the system is expanded to handle multi-class detection or if ranking of predictions becomes necessary. To further clarify how these metrics are computed, we provide the formal definitions below. These equations are based on standard confusion matrix terminology. Accuracy measures the overall proportion of correct predictions--both positive and negative--across all evaluated instances and can be represented with the following equation:  
\[Accuracy\ =\ \frac{TP+TN}{TP+FP+FN+TN} \tag{3}\]  
Precision reflects the reliability of positive predictions by quantifying how many predicted positives are actually correct and can be calculated with the following equation:  
\[Precision\ =\ \frac{TP}{TP+FP} \tag{4}\]  
Recall measures the model's ability to detect all actual instances of the object of interest, which is particularly critical in high-risk applications such as search and rescue.