# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 4 Experiments and Results
### Experiments of State-of-the-Art Algorithms on WAID
#### 4.2.3 Results and Analysis  
The network model evaluations on the WAID dataset are listed in Table 3. The model evaluation results divided by category are shown in Table 4. Overall, the YOLO model exhibits the best performance, with YOLOv5, YOLOv7, and YOLOv8 achieving mAPscores of 95.6%, 97.4%, and 95.8% respectively. Additionally, our proposed SE-YOLO model achieved the highest mAP of 98.3%. Although there is not a significant difference between the Faster R-CNN and SSD models overall, Faster R-CNN slightly outperforms the SSD model. Further analysis reveals that among the four YOLO models, YOLOv5 and YOLOv8 achieve similarly high mAP values. This might be attributed to YOLOv8 being an improvement over YOLOv5, and on the WAID dataset, both models find it relatively straightforward and manageable, resulting in similar performance. For a more vivid illustration of the detection advantages of the SE-YOLO over other baseline methods, we selected plots of the detection results of each method, as shown in Figure 8. From Figure 8, we can see that the SE-YOLO method detects all of the targets with the highest level of confidence, whereas all of the other baselines either fail to detect the targets or have a low level of classification confidence. The SE-YOLO model, an enhancement of YOLOv7 with the introduction of the SEBlock, performs optimally across all categories in the dataset. This indicates that the integration of channel attention mechanisms is effective on the WAID dataset. By comparing SSD and Faster R-CNN models, we find that both one-stage and two-stage approaches face challenges in detecting small objects and densely populated areas. From the model evaluation results based on dataset categories, the two backbone networks of SSD perform poorly on categories other than the \"Seal\" category. This can be attributed to two main reasons: firstly, SSD adopts a relatively simple network structure and multi-scale feature maps, making it difficult to capture features of intricate and complex objects, resulting in weaker detection capabilities for small objects; secondly, since the WAID dataset is an aerial dataset containing a significant number of small and densely packed objects, SSD's use of fixed-size default boxes leads to a weaker ability to handle densely packed objects. Overlapping or closely situated targets might result in inaccuracies in box selection or duplicate detections. In addition, significant differences between the Faster R-CNN and YOLO models are observed in the \"Sheep\" category. This is because Faster R-CNN utilizes an RPN (Region Proposal Network) to generate candidate object boxes. In images where the background is simple and the foreground-background contrast is high, the lack of complexity in the background can make it challenging for the RPN to generate accurate candidate object boxes. This, in turn, affects the subsequent object detection process. In \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **SE-YOLO** & **YOLOv5** & **YOLOv7** & **YOLOv8** & **SSD** & **SSD** & **Faster-RCNN** & **Faster-RCNN** \\  & & & & & **(VGG16)** & **(MobileNetv2)** & **(VGG16)** & **(ResNet50)** \\ \hline Sheep & **0.988** & 0.970 & 0.972 & 0.981 & 0.627 & 0.425 & 0.316 & 0.484 \\ Cattle & 0.987 & 0.984 & 0.988 & 0.971 & 0.824 & 0.721 & 0.622 & 0.761 \\ Seal & **0.994** & 0.980 & 0.980 & 0.984 & 0.926 & 0.821 & 0.684 & 0.922 \\ Camelus & **0.983** & 0.943 & 0.978 & 0.921 & 0.763 & 0.654 & 0.746 & 0.910 \\ Kiang & **0.971** & 0.925 & 0.950 & 0.945 & 0.636 & 0.444 & 0.661 & 0.824 \\ Zebra & **0.977** & 0.932 & 0.964 & 0.944 & 0.660 & 0.479 & 0.629 & 0.771 \\ mAP & **0.983** & 0.956 & 0.972 & 0.958 & 0.739 & 0.591 & 0.610 & 0.779 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Class-wise object detection results on WAID test set. Bold in black is the best result of different models on the same animal category.