## 2 Related Works
### Feature Extraction Modules in Backbone  
The backbone that is utilized to extract high-level semantic features of images is the first part of the DL-based detector. It comprises several feature extraction modules. VGG [33] is one of the earliest backbones for object detection and utilizes 3 \(\times\) 3 convolution layers as the feature extraction module. However, its heavy computation burden and shallow depth hinder the deployability and performance of detectors.  
To solve this problem, [PERSON] et al. [34] introduced a new feature extraction module named a Res Block to deepen the depth of backbones by adding short-cut connections. ResNet based on the Res Block achieves a better accuracy than VGG in the natural scene dataset, with a lower computation burden and deeper depth. The backbone DarkNet53 of YOLOv3 [6] also uses the Res Block as the main feature extraction module. Since then, many feature extraction modules based on the Res Block, such as a ResNeXt Block [35], Res2 Block [36], Dense Block [27] and CSP Block [37], have been improved and developed. The trunk of the ResNeXt Block is split into 32 paths that transform the input from high to low dimensions and back to high dimensions using the same topology, and aggregates them through element-wise addition. Although the ResNeXt Block outperforms the Res Block with fewer parameters and a higher detection accuracy in the natural scene dataset, since the semantic relevance between background and detected objects in ORSIs is stronger than that in NSIs [38], the operation of the ResNeXt Block easily breaks this relevance, and is thus not conducive to the detection performance in ORSIs. The Res2 Block can generate multi-scale features through a hierarchical short-cut connection and increase in receptive fields, thus improving the detection accuracy and reducing the computational consumption. However, its structure with parallel convolution and interactive operations significantly increases the inference time. The Dense Block contains several dense layers. The output of the dense layer is concatenated with its input, and the concatenated feature map serves as the input of the next dense layer. This structure takes full advantage of the short-cut that can better retain the feature and reduce the computation burden. However, the Dense Block will deteriorate in the situation where the background submerges features of detected objects in ORSIs, since the background information is more redundant and complex. Meanwhile, the structure of the Dense Block will reduce its inference speed due to the asymmetry of the input channels number and output channels number for a convolution operation. The CSP Block is the feature extraction module of the backbone CSP DarkNet in YOLOv4. It is mainly composed of several Res units based on a short-cut and a cross-stage part containing a 1 \(\times\) 1 convolution layer. Although this structure can double the number of gradient paths and improve the detection accuracy through a splitting and merging strategy, there is parallel convolution and the problem of the trunk of the CSP Block being stacked alternately by an excessive convolution layer, which significantly increase the degree of network fragmentation and thus decrease the inference speed [39].  
In order to alleviate the shortcomings of the above Blocks, a novel feature extraction module DenseRes Block is proposed in this study to improve the backbone in YOLOv4. Firstly, the input feature map of the DenseRes Block was compressed in order to increase the proportion of object feature information. Then, the series-connected residual structure with the same topology was utilized not only to obtain the high-level semantics of the object feature but also to reduce the computational consumption and inference time. Finally, the feature map output from the residual structure was combined with the input of the DenseRes Block to enhance the semantic relevance between background and detected objects.