# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 4 Results and Analysis
### Experimental Comparison and Analyses
#### 4.3.4 Comparison with the State-of-the-Arts  
In order to further evaluate the method to prove the effectiveness of the proposed method, AP (%) for each category of objects and mAP (%) for multiple categories are both computed. Our method is compared with state-of-the-art methods based on the DOTA dataset. The results are shown in Table 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **mAP(\%)** \\ \hline Baseline & 72.21 \\ Baseline + FEFN & 73.72 \\ Baseline + HAM & 74.69 \\ Baseline + HAM + FEFN & 75.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of ablation experiments. Figure 9: Comparison of detection results. RRPN [26] and R2 CNN [27] directly use the high-level features to predict the category and location information of objects. Due to the large size of remote sensing images and small size objects, the object feature information is seriously lost after multiple operations of convolution and pooling, and their performances are comparably lower than the other methods. ICN [16] proposes image cascade networks to enrich the features and improves mAP to 68.16%. RoI Transformer [28] transfers the horizontal bounding boxes to oriented bounding boxes by learning the spatial transformation, and thus improves mAP to 69.56%. CADNet [17] exploits attention-modulated features, as well as global and local contexts, and improves mAP to 69.9%. SCRDet [18] proposes an inception fusion network and adopts a targeted feature fusion strategy, which fully considers the feature fusion and anchor sampling, and improves mAP to 71.16%. BBAVectors [19] uses the box boundary-aware vectors based on the center point of objects to capture the oriented bounding boxes, and improves mAP to 72.32%. DRN [31] proposes a feature selection module (FSM) to adjust receptive fields in accordance with objects and a dynamic refinement head (DRH) to refine the prediction dynamically in an object-aware manner, and improves mAP to 73.23%. Compared with RRPN, R2 CNN, ICN, RoI-Transformer, CADNet, SCRDet, BBAVectors, and DRN, the mAP of FEHA-OD is improved by 14.01%, 14.35%, 6.86%, 5.46%, 5.12%, 3.86%, 2.7%, and 1.79%, respectively. FEHA-OD achieves the highest mAP. In detail, FEHA-OD is effective in detecting some large objects, such as BD, BC, and RA. For other large objects, such as TC and SP, FEHA-OD also maintains a good detection performance. These objects with large scale usually appear together with some small objects, such as SV and LV. FEHA-OD also has good detection performance for multi-scale objects. The main reason for this is that the FEFN effectively uses the fused features from different layers and facilitates multi-scale object detection. Moreover, the detection accuracy of SV, LV, ST, SH, and other small objects with complex backgrounds is very good. The main reason for such effective detection is that the HAM module effectively highlights object features and prevents small objects from being submerged by noises. In particular, objects with arbitrary orientations can also be detected very well. Some visual detection results of FEHA-OD on the DOTA dataset are shown in Figure 10. The objects belonging to different categories are labeled by rectangles with different colors. Considering both very large objects, such as roundabout (RA) and ground track field (GTF), or small objects submerged by background noise, such as a small vehicle (SV), FEHA-OD can achieve excellent detection performance. Even if these objects present various scales and orientations, seriously disturbed by noises, FEHA-OD locates and classifies them accurately.