# BFE-Net: Bidirectional Multi-Scale Feature Enhancement for Small Object Detection
## 4 Experiments
### Ablation Study  
In this section, we conduct extensive ablation experiments to analyze the effects of individual components in our proposed method. This paper also analyzes the effect of each proposed component of BFE-Net on COCO val2017. The purpose of this study is as follows. To analyze the importance of each component in BFE-Net, we gradually apply a bidirectional feature pyramid network, High-level Guided Model, and Dilated Feature Enhancement to the model to verify its effectiveness. Meanwhile, the improvements brought by the combination of different components are also presented to demonstrate that these components complement each other. The baseline method for all ablation studies is ResNet50. All results are shown in Table 3.  
From the experimental data in the table, it can be seen that the three structures proposed in this paper have different degrees of improvement in the accuracy of the standard RetinaNet. Combining the improved strategies in this paper can effectively improve the detection performance of the detection algorithm for small targets. After adding the bidirectional feature pyramid structure, the accuracy rate has increased by 1.1%, and the accuracy of small targets (AP\({}_{\text{S}}\)) has reached 15.1%, with an improvement of 1.2%. In addition, after adding HGM and DFE, the accuracy of small targets (APs) is increased by 0.4% and 0.8%, respectively, which also shows that AGH is of great help to the recognition of small-scale targets by fully extracting shallow features. The accuracy of target detection of other sizes is also improved to varying degrees. The overall accuracy rate has increased from 32.5% to 34.6%, and the accuracy rate of small targets has achieved a very meaningful improvement, from 13.9% to 16.7%, a 2.8% increase. To verify the effectiveness of HGM, the following ablation experiments were performed. R-C6 represents that C5 directly obtains C6 through a 3 \(\times\) 3 convolutional layer with stride 2, which is the same as in RetinaNet [11]. H-C6 represents C6 obtained by means of HGM. The results are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**R + B + D** & **R-C6** & **H-C6** & **AP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** \\ \hline \(\surd\) & & & 34.1 & 52.1 & 36.7 & 15.9 & 36.9 & 47.4 \\ \(\surd\) & \(\surd\) & & 34.4 & 52.9 & 37.1 & 16.4 & 37.2 & 47.6 \\ \(\surd\) & & \(\surd\) & 34.6 & 52.9 & 37.2 & 16.7 & 37.3 & 47.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation studies of Dilated Feature Enhancement on COCO val-2017. B + R + H: RetinaNet + BidiFPN + HGM. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Backbone** & **AP** & **AP\({}_{\text{S}}\)** & **FPS** \\ \hline Faster R-CNN w FPN [16] & ResNet-101 & 36.2 & 18.2 & 2.4 \\ SSD513 [6] & ResNet-101 & 31.2 & 10.2 & 8 \\ DSSD513 [14] & ResNet-101 & 33.2 & 13.0 & 6.4 \\ RetinaNet [11] & ResNet-101 & 34.4 & 14.7 & 11.1 \\ EfficientDet-D0 [12] & EfficientNet & 34.6 & - & 10.3 \\ BFE-Net(ours) & ResNet-101 & 36.3 & 17.9 & 9.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Speed on COCO test-dev. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**RetinaNet** & **BidiFPN** & **HGM** & **DFE** & **AP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** \\ \hline \(\surd\) & & & & 32.5 & 50.9 & 34.8 & 13.9 & 35.8 & 46.7 \\ \(\surd\) & \(\surd\) & & & 33.4 & 51.6 & 35.9 & 14.6 & 36.3 & 47.0 \\ \(\surd\) & \(\surd\) & \(\surd\) & & 34.0 & 52.3 & 36.5 & 15.1 & 36.8 & 47.5 \\ \(\surd\) & \(\surd\) & & \(\surd\) & 34.1 & 52.1 & 36.7 & 15.9 & 36.9 & 47.4 \\ \(\surd\) & \(\surd\) & \(\surd\) & & 34.6 & 52.9 & 37.2 & 16.7 & 37.3 & 47.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of each component on COCO val-2017. BidiFPN: bidirectional feature pyramid network; HGM: High-level Guided Model; DFE: Dilated Feature Enhancement.