# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 2 Related Work
### Various Orientations  
Due to various orientations and dense distribution of objects, classical object detection methods adopting the horizontal bounding boxes easily result in a large IOU (intersection over union) between the horizontal bounding boxes and the real oriented bounding boxes of objects. If a horizontal bounding box is used to label an object with an oblique direction, the object is easily filtered out by non-maximum polar suppression, resulting in misdetection. Therefore, researchers proposed oriented bounding boxes and achieved better detection performance. The classic methods include RRPN [26], R2 CNN [27], etc. RRPN improved the RPN network [4] by adding rotated anchors with different angles, and developed a rotated RPN network. R2 CNN regressed the parameters of the oriented bounding box by using several ROI pooling operations with different sizes. [PERSON] et al. [28] proposed RoI Transformer to learn the transformation from a horizontal bounding box into an oriented bounding box and extracted the rotation-invariant features for object classification and location regression. However, the above methods are only based on angle to capture the oriented bounding boxes.  
Actually, small angle change has little influence on the total loss in training, but it may lead to a large IOU difference between the prediction box and the ground-truth box. Therefore, [PERSON] et al. [19] used box boundary-aware vectors (BBAVectors) to describe the oriented bounding boxes. Instead of regressing the angles, it regressed the four vertices of the object pointing from the object center to the four edges, and then captured the oriented bounding boxes. Compared with the methods that learn the angles of an object, the BBAvectors method achieves better performance.  
The above methods focus on the difficult problems of object detection in remote sensing images, but there are still some deficiencies. First, when they obtain multi-scale features, they usually aggregate the feature maps of different layers directly. However, the receptive fields of different scale feature maps are different, and the representation capability of multi-scale features will be weakened because of the direct aggregation of the feature maps in different layers. Considering the characteristics of the fused feature layers, and effectively using the fused features, even enhancing original features, is important. Secondly, the existing methods usually consider a certain attention mechanism, such as channel attention or spatial attention. However, a hybrid attention can highlight object features more effectively. Finally, for objects with arbitrary directions, most existing methods get oriented bounding boxes through angle regression, but angle optimization cannot guarantee that the IOU error is minimized. Localizing objects by using BBAVvectors can better optimize IOU errors and improve detection performance. Therefore, this paper proposes a feature enhancement fusion network (FEFN) and a hybrid attention mechanism (HAM) module, and uses BBAVvectors to overcome these deficiencies, thereby achieving better detection performance.