# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 1 Introduction  
Remote sensing images are obtained when we observe the earth's surface from different angles and heights through aero or space aircrafts. Thus, the image scenes are usually complex and diverse, and there are many noises for long-distance surveillance. In addition, the objects in remote sensing images have large degrees of freedom in scale, orientation, and density. At present, remote sensing images are widely used in many fields, such as traffic monitoring, disaster assessment, urban management, and so on [1]. With the development of remote sensing techniques, object detection in remote sensing images is gradually attracting attention. Object detection is a very challenging task in computer vision. The in-depth study of deep learning has supported considerable progress. Many detectors, such as R-CNN [2], Fast R-CNN [3], Faster R-CNN [4], SSD [5], YOLO [6], CornerNet [7], FCOS [8], CenterNet [9], and other classic object detection methods, have been proposed. However, when these methods are directly applied to remote sensing images, the detection performance will decrease obviously. The main reason for this is that there are obvious differences between remote sensing images and general natural images, which are as follows: (1) In remote sensing images, objects vary greatly in scale. There are many object categories in remote sensing images, and there may be large-scale differences among objects of different categories. As shown in Figure 0(a), the scale of a soccer ball field is usually more than ten times larger than a tennis court, and hundreds of times larger than a small vehicle,even existing in the same picture. (2) Small objects are easily submerged by background noises. Remote sensing images have complex backgrounds and usually contain many background noises. As shown in Figure 1b, we take two points on the sea ripples and the edges of the ship, and then zoom in on the neighborhoods, respectively. The two white circles demonstrate the neighborhoods, and they have very little contrast. Small objects are easily disturbed by background noises, which leads to misdetection or false detection. (3) Objects show various orientations and are often densely arranged. For example, there are 16 densely parked vehicles in Figure 1c. When we use horizontal bounding boxes to label them, five of them are filtered out by NMS (non-maximum suppression), and 31.25% of the vehicles are missed. Therefore, the main problems are summarized as scale, noise, and orientation. For these challenging problems, researchers have carried out a series of related works for object detection in remote sensing images. For multi-scale variations, many methods obtain multi-scale features from different layers to improve the accuracy of object detection, such as RADet [10], a refined feature pyramid network with top-down feature maps, SGD (Semantic Guided Decoder) [11], aggregating multiple high-level features; for background noises, attention mechanisms have been verified to suppress them, such as EFM-Net [12], which includes a background filtering module based on attention mechanisms to suppress the background interference and enhance the attention of objects simultaneously, AMGM (Attention Map Guidance Mechanism) [13], which optimizes the network to keep the identification ability, and GABAM (Generating Anchor Boxes based on Attention Mechanism) [14], which generates attention map to obtain diverse and adaptable anchor boxes; for arbitrary orientation, many methods use oriented bounding boxes to locate objects, which effectively eliminate the mismatching between bounding boxes and corresponding objects. However, when facing the various problems, the existing methods still cannot effectively deal with the above complex situations, and the detection accuracy is still low. Therefore, this paper proposes an object detection method based on feature enhancement and hybrid attention (FEHA-OD) for remote sensing images. To address the detection  
Figure 1: Characteristics of remote sensing images.