## 3 Experiments and Analysis
### Comparison and Analysis of Ablation Experiments  
Our method is mainly composed of two modules: the TVF and the GHPNet networks. Firstly, in order to verify the effectiveness of the TVF algorithm, Table 5 gives the target detection comparison results of median and mean methods used for background estimation. It can be seen from Table 5 that compared with the median method, the F1 value of target detection using the mean method for background estimation is increased by 9.1%, the frame rate is also much faster. The experimental results demonstrate the effectiveness of the TVF algorithm, using the mean method for background estimation. Secondly, in order to verify the contribution of each module to detection performance, Figures 8 and 9 show the target detection subjective results using TVF and GHPNet combined with TVF on our captured image and the image from the TTV dataset, respectively. It can be seen from the experimental results that the TVF algorithm can accurately detect a single flying bird that exists independently in the space. However, when multiple flying birds are clustered together with a certain degree of overlap and occlusion in the image space, multiple flying birds will be detected as a single individual. For the problem that the TVF algorithm cannot accurately distinguish the individual birds in the aggregation area, GHPNet successfully  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Year** & **F1 (\%)** & **FPS** \\ \hline U-Net [29] & 2015 & 77.3 & 9 \\ Faster R-CNN [17] & 2017 & 76.7 & 7 \\ Mask R-CNN [18] & 2017 & 80.5 & 5 \\ YOLOv4 [24] & 2020 & 88.0 & 31 \\ DRU-Net [31] & 2021 & 86.5 & 5.2 \\ DNA-Net [32] & 2021 & 88.2 & 4.4 \\ Our method without \(1\times 1\) convolution & 2022 & 90.5 & 18.9 \\ Our method with \(1\times 1\) convolution & 2022 & 90.5 & 21.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Frame rate comparison results.  
Figure 7: Subjective detection results of several methods, (**a**) original image; (**b**) enlarged version of the selected region; (**c**) the detection results of Mask-RCNN [14]; (**d**) the detection results of DRU-Net [16]; (**e**) the detection results of DNA-Net [32]; (**f**) the detection results of Yolov4 [12]; (**g**) the detection results of our method.  
perceives the individual Gaussian heatmap effectively, thereby realizing the high-precision detection of dense birds.  
Lastly, it can also be seen from the ablation experiment results in Table 6 that the detection performance of GHPNet is improved by at least 7.9% on the F1 value of the two test experimental datasets compared to the TVF algorithm. Additionally, compared with not using maximum-no-pooling, using maximum-no-pooling improves the F1 performance by 0.9% and 3.4% on our captured dataset and the TIV dataset, respectively. It can be seen that for a denser spatial distribution of birds, using maximum-no-pooling can maintain the features of small targets in deeper network layers to avoid the loss of them.  
Figure 8: Comparison of the first-stage and the second-stage detection results on our dataset, (**a**) the detection result of TVF; (**b**) the detection result of GHPNet combined with TVF.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **TVF** & **P (\%)** & **R (\%)** & **F1 (\%)** & **FPS** \\ \hline Median & 69.2 & 63.4 & 66.2 & 10 \\ \hline Mean & 78.0 & 72.7 & 75.3 & 71 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison detection results of median and mean mode used in TVF on our dataset.  
Figure 9: Comparison of first-stage and second-stage detection results on TIV dataset, (**a**) the detection result of TVF; (**b**) the detection result of GHPNet combined with TVF.