## 3 Experiments and Analysis
### Comparisons with the Baseline Methods  
In order to verify the performance of the proposed method, we compare our method with several state-of-the-art methods such as Faster R-CNN [17], Mask R-CNN [18], YOLOv4 [24], U-Net [20], DRU-Net [31], and DNA-Net [32]. From the above datasets, we randomly extract 500 images containing different numbers of birds and bats as training samples for all eight comparison methods. We firstly show the detection results of all methods on the bird dataset produced by us in Table 2. As shown in Table 2, our method obtains the highest P, R, and F1 score performance, 91.2%, 89.8%, and 90.5%, respectively, which are much higher than the other five methods. In terms of the F1 score, our method is the best, while DNA-Net [32] and YOLOv4 [24] are second, and Faster R-CNN [17] is the worst. DNA-Net [32] has a comparable performance to YOLOv4 [24] in F1 scores. Although the F1 scores of these two methods are improved by at least 6.0% compared to Mask R-CNN [18] and U-Net [29], they are still 1.3% and 1.5% lower than our method. We secondly show the detection results on the bat video sequences of the dataset TIV in Table 3. As shown in Table 3, for the more densely distributed bats, P, R, and F1 scores of all methods are worse than the corresponding results in the relatively sparse case in Table 2, with an average drop of 2.7 percentage. For bats that appear densely in dataset TIV, Faster R-CNN [17] has the worst detection performance, followed by U-Net [29]. The performance \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Backbone**} & \multicolumn{3}{c}{**Detection (\%)**} \\ \cline{3-5}  & & & **P** & **R** & **F1** \\ \hline U-Net [29] & 2015 & U-Net & 82.6 & 79.7 & 81.1 \\ Faster R-CNN [17] & 2017 & VGG16 & 80.3 & 73.4 & 76.7 \\ Mask R-CNN [18] & 2017 & ResNet-101-FPN & 83.3 & 77.9 & 80.5 \\ YOLOv4 [24] & 2020 & CSPDArnet-53 & 89.4 & 86.6 & 88.0 \\ DRU-Net [31] & 2021 & U-Net & 87.0 & 86.1 & 86.5 \\ DNA-Net [32] & 2021 & U-Net & 89.0 & 87.4 & 88.2 \\ Our method & 2022 & VGG16+U-Net & 91.2 & 89.8 & 90.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of detection performance on the bird dataset. of Mask R-CNN [18] and DNA-Net [32] is slightly better. Compared with the previous four methods, the performance of DRU-Net [31] and YOLOv4 [24] has been greatly improved, but it is still worse than our method. The state-of-the-art detection performance shows that our method can detect dense small objects well. To further demonstrate the advantages of our method, we show the subjective performance comparisons in Figure 7. Three test images and their expanded versions of the red boxes are shown in Figure 7a,b, respectively. The detection results corresponding to different methods are given in Figure 7c-g. The actual numbers of birds in the left and the right regions cropped from the first image in Figure 7a are 35 and 47, which is shown in the first and the second column in Figure 7b, respectively. As shown in the first and the second columns in Figure 7c-g, respectively, the number of birds detected by Mask R-CNN [18] is 30 and 41, with 15 and 35 detected by DRU-Net [31], with 27 and 37 detected by DNA-Net [32], with 28 and 42 detected by YOLOv4 [24], and with 32 and 45 detected by our algorithm. The actual number of birds in the second image in Figure 7b is 38, as shown in the third column of Figure 7c-g, the number of the detected birds of Mask-RCNN [14], DRU-Net [16], DNA-Net [32], Yolov4 [12], and our method is 33, 30, 34, 34, 37, respectively. The number of birds in the third image in Figure 7b is 74. For the case where the birds appear more densely, the detection numbers of Mask-RCNN [14], DRU-Net [16], DNA-Net [32], Yolov4 [12], and our method are 57, 62, 67, 80, and 73, as shown in the fourth column in Figure 7c-g, respectively.