## 3 Methods
### Attention-Based Feature Extraction Network  
Darknet53, the backbone of YOLOv3, is mainly composed of residual units, and because of the way these residuals are combined, Darknet53 can be trained effectively even when stacked to 53 layers, with no gradient explosions or gradient disappearance. However, because the residual block stacking is very deep, the training is slow, and the shortcut in the individual residual blocks causes the perceptual field to capture only detail information and not global characteristics. Thus, in complex scenes, the features in each layer are not extracted sufficiently or effectively, and complex scenes in remote sensing image object detection and the simple stacking of residual units to deepen the network do not significantly improve the feature extraction ability. In order to solve the problem, which is difficult to extract features under the complex background of remote sensing images, we add the spatial groupwise enhancement (SGE) attention module to the residual unit. SGE is based on SE-Net and combined with the idea of grouping so that it is a lightweight attention module that increases the classification and detection performance with nearly no increase in the number of parameters or the computational cost. A complete feature is composed of many subfeatures, which are distributed in groups in each layer; however, these subfeatures are all processed in the same manner and are all affected by background noise, which can lead to incorrect recognition and localization results. Therefore, the addition of the SGE module can generate an attention factor in each group, allowing the importance of each subfeature to be obtained and each group to learn and suppress noise as follows:  
1. The feature map is divided into G groups based on the channel dimension;
2. The attention factor of each group is determined;
3. Global average pooling is performed on each group to obtain the vector g;
4. The vector g is element-wise dotted with the original group feature;
5. The vector is normalized, sigmoid activated, and element-wise dotted with the original group feature;
6. Finally, the enhanced feature map is generated.  
A feature map was obtained from the original image after continuous processing of multiple convolutions, and then, it is divided into several groups along the channel dimension and processed by SGE module. The attention factor of each group of features was obtained and mapped to the corresponding feature map. Finally, after semantic feature enhancement, the feature map was generated. The SGE structure diagram is shown in Figure 3.  
Due to the light weight of the SGE module and its effectiveness for higher-order semantic features, the SGE module can be perfectly integrated with Darknet53. We add the SGE module to the residual unit to improve the ability of the backbone network to extract features in complex scenes. In particular, the original feature map is convolved, batch normalized and activated by the activation function, and after the second convolution and batch normalization, the feature enhancement is performed by the SGE module, and the enhanced feature map is summed with the original feature map by shortcut edges and then activated by the activation function. Figure 4 shows the SGE module after it has been inserted into the residual block.  
The residual units with attention modules are continuously stacked to form the backbone SGEDarknet53, whose structure is shown in Table 1.