## 3 Proposed Methods
### Improvement in the Backbone  
YOLOv4 adopts a CSP Block, shown in Figure 4a, to extract features of images in the backbone. Although the CSP Block performs well in detection accuracy, the structure of the CSP Block containing a parallel convolution operation for reusing the feature of the 'Input' and excessive convolution layers caused by 'Res Unit' takes up a large amount of computing resources and inference time [39]. Aiming at this problem of the CSP Block, we proposed a DenseRes Block, shown in Figure 4b, and employed it in the backbone for feature extraction. The DenseRes Block is only composed of several series-connected \(3\times 3\) convolution operations \(f_{3\times 3}^{(i)}\)\((i=1,2,\cdots,n)\) and short-cut connections based on residual learning. \(y_{i}\) is the output feature map of \(f_{3\times 3}^{(i)}\). For the feature map \(Input\in\mathbb{R}^{W\times H\times C}\), \(W\), \(H\) and \(C\) indicate the height, width and channel number of the map, respectively. Since the feature of detected objects in ORSIs is easily overwhelmed by that of the background when transmitted, we utilized a feature map with fewer channels as the output of the first convolution operation to compress the '\(Input\)' to focus on object features and reduce the proportion of background information. Therefore, the feature map \(y_{1}\in\mathbb{R}^{W\times H\times G}\) was computed by  
\[y_{1}^{W\times H\times G}=f_{3\times 3}^{(1)}\Big{(}Input^{W\times H\times C} \Big{)} \tag{1}\]  
where \(C=n\times G\), \(f_{3\times 3}^{(1)}\) contains the \(3\times 3\) convolution layer that compacts the number of channels from \(C\) to \(G\), the BN layer and the leaky ReLu activation function. If \(n=1\), the DenseRes Block is the same as the Res Block. When \(n>1\), the DenseRes Block will compress the '\(Input\)' and make a feature extraction. It was proven in Ref. [39] that the following  
Figure 4: The structure comparison between CSP Block (**a**) and DenseRes Block (**b**). operations can effectively reduce the memory access cost and the inference time of the model: (1) the input channel and output channel of the convolution layer should be equal as much as possible; (2) the number of fragmented operators (i.e., the number of individual convolution or parallel operations in one building block) should be reduced. Therefore, \(y_{j}(1<j\leq n)\in\mathbb{R}^{W\times H\times G}\) could be designed as  
\[y_{j(1<j\leq n)}^{W\times H\times G}=y_{j-1}^{W\times H\times G}\oplus f_{3 \times 3}^{(j)}\Big{(}y_{j-1}^{W\times H\times G}\Big{)} \tag{2}\]  
where \(f_{3\times 3}^{(j)}(1<j\leq n)\) contains the \(3\times 3\) convolution layer with the same number \(G\) of input and output channels, the BN layer and the leaky ReLu activation function. \(\oplus\) indicates the element-wise addition. From the comparison between the CSP Block and the DenseRes Block shown in Figure 4, the output of each 'Res Unit' in the CSP Block will go through two convolution layers with different kernel sizes, whereas that of each '\(y_{i}\)' in the DenseRes Block only goes through one \(3\times 3\) convolution layer. Therefore, the fragment degree can be decreased. Moreover, we used a short-cut based on residual learning to connect \(y_{j}(1<j\leq n)\) and \(y_{j-1}(1<j\leq n)\) for the problem of feature loss in the process of feature extraction. In ORSIs, there will be potential semantic relevance between the object and the background [21; 38]. For example, cars and airplanes tend to park on land whereas ships tend to sail on the sea, and bridges are built over water whereas overpasses are built over land.