## 4 Data Diversity of UAV
### Methods Applied to the UAV Dataset
#### 4.3.1 Machine Learning and Deep Learning  
Deep learning models, such as convolutional neural networks (CNNs) [64], are widely used for tasks like object detection, segmentation, and classification. Some examples are as follows:  
* The RescueNet dataset employs models like PSPNet, DeepLabv3+, and Attention UNet for semantic segmentation to assess disaster damage. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Dataset Name** & **Experimented Methods on Dataset** & **Analysis of Results** \\ \hline UAVid [11] & FCN-8s [53], Dilation Net, U-Net [54], MS-Dilation Net & MS-Dilation Net achieved the highest mean IoU score of 57.3\% with pre-training and feature space optimization, demonstrating the best performance among the models evaluated. \\ \hline VRAI [11] & MGN, RAM, RNN-HA, Ensemble methods (e.g., ID Classification Loss, Triplet + ID Loss), Novel methods (Multi-task, Multi-task + Discriminative Parts) & The multi-task model with discriminative parts achieved the highest mAP (78.63\%) and CMC-1 (80.30\%). The models using Triplet + ID Loss also showed high performance, particularly with Resnet-101 and Resnet-152 backbones. \\ \hline FOR-instance [13] & None, as the paper is solely focused on constructing the dataset and explaining how to utilize it for model. \\ \hline VERI-Wild [14] & GoogLeNet [55], Triplet [56], Softmax [57], CCL [58], HDC [59], Unlabeled GAN [60,61], EN (Embedding Network with Triplet and Softmax Loss), FDA-Net \(\ominus\) Att, FDA-Net & FDA-Net consistently outperforms the other models across different settings, achieving the highest mAP (35.11\%) and match rate (R = 1 of 64.03\% for small dataset). The proposed FDA-Net model demonstrates its effectiveness in vehicle re-identification tasks. \\ \hline UAV-Assistant (UAA) [15] & Singleshotpose [62], Direct, IoU-based experimental methods (e.g., 10.1, I0.2, 10.1-0.4, G0.1, S0.1, S0.2), Generalized IoU-based method (Gauss0.1) [63] & Gauss0.1 showed the best overall performance, particularly in the 6D Pose-5 and 6D Pose-10 metrics. Metrics such as NPE, OE, and CPE were used, with lower values indicating better performance and higher values for Acc5 and Acc10 indicating better performance. \\ \hline KITE [16] & Baseline Systems (Unadapted System, Domain-Specific System), Domain Adaptation (Text-Only Adaptation, Rescoring), Multi-Modal Experiments (Text and Visual Information) & Domain adaptation and multi-modal approaches significantly improved the performance of speech recognition systems for UAV control. The Unadapted System had a WER of 56.2\%, while the Domain-Specific System achieved 11.7\%. \\ \hline UAV-Gesture [17] & Pose-based CNN (P-CNN) & P-CNN achieved an overall accuracy of 91.9\% for gesture recognition. The dataset included 119 video clips, 37,151 annotated frames, and 13 gestures, providing a robust resource for gesture and action recognition research. \\ \hline DarkTrack2021 [19] & Novel ensemble method: SCT & The full implementation of SCT (Spatial-Channel Transformer) with all components enabled showed the highest improvement in tracking performance, with success rate and precision gains of 13.3\% and 15.4\%, respectively. \\ \hline UAVDark135 [18] & ADTrack, State-of-the-art trackers (e.g., AutoTrack, SiamFC++, ARCF-HC, SiamRPN++) & ADTrack outperformed all other models in both bright and dark conditions, showing superior performance with the highest DP and AUC scores on the UAVDark135 dataset. \\ \hline BioDrone [20] & KeepTrack, UAV-KT, Generic SOT Trackers & UAV-KT, designed for flapping-wing UAVs, showed a 5\% improvement over KeepTrack in precision, normalized precision, and success scores. Generic SOT Trackers were compared for robustness and performance across various conditions. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of experimented methods and results on different datasets. * The UAVid Dataset presents deep learning baseline methods like Multi-Scale-Dilation net.