## 3 Experiments and Results
### Ablation Studies on Different Structures  
We conducted comparative experiments on the test dataset to analyze the effect of each component of FENet. The overall ablation studies are reported in Table 2. In experiments, we used the FCOS as the baseline and the ResNet-101 as the backbone. We gradually added EFM, context-aware strategy, and CIoU loss on the baseline. The baseline means the original FCOS architecture without using any components. The last row of Table 2 shows that FENet increases by 4.7% AP compared with the baseline. The improvement brought by the components demonstrates the effectiveness of our proposed method.  
The method of baseline-1 in the second row of Table 2 represents the detection performance of the FCOS detector with EFM. It shows that the proposed EFM with the integration map X improves the AP by about 2.2%. Therefore, multiscale feature enhancement is significant for PSSs detection. To intuitively illustrate the effects of EFM, we applied the Grad-cam [38] on enhanced feature maps output from FENet. Figure 7 shows that the information extracted by EFM becomes more abundant and accurate. The enhanced maps highlight locations of objects in the feature maps. Our model can substantially improve the discrimination ability of feature maps and suppress redundant context information. In our feature pyramid layers, each layer is used for predicting objects with different scales. Figure 7b-d presents the E3, E4, E5 of FENet. It can be seen that the lower layers obtain more attention when predicting smaller objects, and the higher layers obtain more effective information when predicting larger objects. The detection performance is significantly improved by assigning objects of different sizes to different feature pyramid layers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **EFM** & **Context-Aware Strategy** & **CIoU Loss** & **AP** \\ \hline Baseline & & & & 74.0 \\ Baseline-1 & \(\surd\) & & & 76.2 \\ Baseline-2 & & \(\surd\) & & 75.9 \\ Baseline-3 & & & \(\surd\) & 75.4 \\ FENet & \(\surd\) & \(\surd\) & \(\surd\) & 78.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance of different components of our proposed method.  
Then, we implemented a context-aware strategy on the baseline. The method of baseline-2 in the third row of Table 2 shows that the context-aware strategy increases the AP by 1.9%. The ablation studies illustrate that context-aware strategy can capture effective regions and locate objects more precisely.  
Additionally, we conducted experiments with different _IoU_ loss functions. The method of Baseline-3 in the fourth row of Table 2 presents that the AP is increased 1.4% by CIoU loss function. The original bounding box regression uses _IoU_ loss, which only works well for the cases of overlapping with target boxes [13]. In order to further evaluate the performance of CIoU loss, we applied _IoU_ loss, generalized-_IoU_ (GIoU) [39] loss, and CIoU [37] loss on FENet. In Table 3, it can be seen that the proposed network reaches the best performance when CIoU loss function is adopted. Compared with _IoU_ loss and GIoU loss, the AP of the model using CIoU loss is improved by 2.2% and 0.8%, respectively. The results indicate that CIoU loss is more conducive for model training.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Regression** & **AP** \\ \hline FENet-1 & _IoU_ & 76.5 \\ FENet-2 & GIoU & 77.9 \\ FENet-3 & CIoU & 78.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of the detection performance among different loss functions of bounding box regression.  
Figure 7: The effect of EFM through the visualizing attention map: (**a**) examples of the input images; (**b–d**) the heatmaps of E3, E4, E5 corresponding images, respectively.