## 3 Experiments and Results
### Comparison to State-of-the-Art Methods  
We compared our proposed method with anchor-based methods (FPN [9], RetinaNet [36], and ADNet [28]) and anchor-free methods (CenterNet [25], FCOS [23]) on the same training set, shown in Table 5. All methods were implemented on Pytorch. As can be seen from Table 5, ADNet achieves the best AP of 79.2% higher by 6.8% and 6% over RetinaNet and FPN, and FENet achieves the best AP of 78.7% higher by 6.6% and 4.7% over the CenterNet and FCOS. RetinaNet and FPN are the effective anchor-based methods. RetinaNet uses focal loss function to eliminate class imbalance and enhance feature learning of difficult samples. However, RetinaNet cannot learn the critical features of composite objects and effectively distinguish positive samples and negative samples in some cases, resulting in lower accuracy. FPN only uses the basic CNN, which cannot solve the problem of composite object detection. In addition, FPN needs to set suitable anchors for detection. CenterNet only uses a center point to represent the object and takes a long time to train. These methods need to enhance the representation ability of models for composite objects. In particular, we compared the FENet with ADNet on the same training dataset. The experiments show that the AP obtained by FENet is close to the AP obtained by ADNet, which indicates that FENet can achieve acceptable AP without prior knowledge about anchors. ADNet requires more pre-processing of data during training, which results in a huge amount of work. At the same time, ADNet requires statistical analysis of the bounding boxes when the model is trained on a new dataset, which limits the extension and application of the model. Although the accuracy of FENet is slightly lower, it is more flexible in bounding box regression and does not require more processing of data before training. The detection results demonstrate that FENet is superior to the typical object detectors. Figure 8 presents some detection results of a typical anchor-based method and a anchor-free method on the test set. Figure 8a-c shows the results of FPN, (d-f) shows the detection results of FCOS, and (g-i) shows the detection results of FENet. By comparing (a,d), (b,e) in Figure 8, we can see that FPN and FCOS detect some small buildings containing a vacant lot as PSSs. It indicates that these methods have no ability to model the critical features \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Radius** & **0.5** & **1.0** & **1.5** & **2.0** & **2.5** & **3.0** & **All Area** \\ \hline AP & 69.1 & 75.7 & 77.2 & 78.7 & 77.1 & 75.5 & 76.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model performance of using different radii in the context-aware strategy. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **AP** \\ \hline RetinaNet & 72.4 \\ FPN & 73.2 \\ ADNet & 79.2 \\ CenterNet & 72.1 \\ FCOS & 74.0 \\ FENet & 78.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detection results of different methods. of PSSs, which leads to misjudgments. In addition, these methods still fail to distinguish foreground from background in rural areas, as shown in Figure 8c,f. The results indicate that FPN and FCOS do not perform well and cannot correctly detect the objects in complex backgrounds. The PSSs cannot distinguish from small spaces and clustered buildings. After adding EFM and suitable bounding box regression strategy, FENet can detect the whole object effectively. The experimental results convincingly prove that FENet can locate PSSs precisely by capturing the critical features and excluding false positives. Compared with anchor-based  
Figure 8: Results of different methods on the test set: (**a-c**) are the detection results of the FPN detector, (**d–f**) are the detection results of FCOS, and (**g-i**) are the detection results of our proposed method. The ground truth boxes are plotted in green, and the detection results of the proposed method are plotted in red.