# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 4 Experiments and Results
### Experiments of Cross-Dataset Validation  
We conducted a cross-dataset generalization experiment to evaluate the generalization ability of the WAID dataset. This experiment aimed to determine if models trained on different datasets could perform well on new datasets. Furthermore, this experiment can illustrate the applicability and usefulness of our proposed dataset in the field of UAV wildlife detection. Through this experiment, we aimed to assess the effectiveness and robustness of the trained models and training datasets in broader contexts. Due to differences in animal categories and aerial heights between existing aerial imagery datasets like AED and Arctic Seal, and the WAID dataset, we selected various source-specific aerial wildlife datasets with the same categories. We combined these datasets to create a small-scale multi-category dataset called the Multi-Source Aerial Wildlife Image Dataset (MSAWID), which was then compared against the WAID dataset. The SE-YOLO model was selected for verification experiments, and the mean Average Precision (mAP) was used as the evaluation metric.  
The results of the experiment are presented in Table 6. We found that the model trained on the WAID dataset achieved a mAP of 39.5% on the MSAWID dataset, indicating that the model retained relatively high detection accuracy on unseen data sources. However, this score was 28.1% lower than the mAP achieved by a model trained directly on MSAWID. This decrease in performance can be attributed to the uneven distribution of categories in MSAWID, where some categories had fewer images. Objects in these limited images could be influenced by various factors like natural environment, occlusion, and aerial height, leading to significant differences in the learned features between the models trained on different datasets and potentially resulting in detection errors. In summary, the generalization ability of the WAID dataset benefits from its relative stability across cross-dataset experiments. Nevertheless, challenges might arise when dealing with multi-source datasets that have different origins and uneven category distributions. This finding emphasizes the importance of dataset diversity and class balance in enhancing model generalization. Additionally, given that the annotation quality in WAID is notably higher than that in MSAWID, some false positives in the comparison could stem from the detection of animals  
Figure 9: The detection examples of case study. The targets in yellow boxes are the minimal objects of about 20 pixel points that can be detected. The targets in fluorescent blue boxes are the minimal objects less than 20 pixel points can not be detected.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & _Precision_ & _Recall_ & _mAP_ \\ \hline YOLOv7 & 0.796 & 0.875 & 0.822 \\ SE-YOLO & **0.929** & 0.847 & **0.926** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The recognition performance of SE-YOLO and YOLOv7 on the minimal targets.  
that were often unannotated or overlooked in MSAWID due to their small sizes. As for MSAWID, models trained on it achieved only 15.3% mAP on the WAID dataset (an 83% drop in comparison to models trained on WAID). This reflects WAID's greater diversity and challenges compared to the available datasets, making it well-suited for real-world wildlife detection scenarios.