# Remote Sensing Image Target Detection Method Based on Refined Feature Extraction
###### Abstract  
To address the challenges posed by the large scale and dense distribution of small targets in remote sensing images, as well as the issues of missed detection and false detection, this paper proposes a one-stage target detection algorithm, DCN-YOLO, based on refined feature extraction techniques. First, we introduce DCNv2 and a residual structure to reconstruct a new backbone network, which enhances the extraction of shallow feature information and improves the network's accuracy. Then, a novel feature fusion module is employed in the neck network to adaptively adjust the fusion weight for integrating texture information from shallow features with deep semantic information. This targeted approach effectively suppresses noise caused by extracting shallow features and enhances the representation of key features. Moreover, the normalized Gaussian Wasserstein distance loss, replacing Intersection over Union (IoU), is used as the regression loss function in the model, to enhance the detection capability of multi-scale targets. Finally, comparing our evaluations against recent advanced methods such as YOLOv7 and YOLOv6 demonstrates the effectiveness of the proposed approach, which achieves an average accuracy of 20.1% for small targets on the DOTAv1.0 dataset and 29.0% on the DIOR dataset. remote sensing images; target detection; refinement feature extraction; deformable convolution; SimAM attention mechanism +
Footnote †: journal: Medical Research and Engineering  
**Citation:** [PERSON]; [PERSON]. illumination in remote sensing images, along with the complexity and variability of backgrounds, presents a great challenge for traditional convolutional structures in extracting refined target features [14]. Conventional feature fusion structures, such as the widely adopted Feature Pyramid Network (FPN) [15], tend to introduce noise from shallower layers, thereby submerging the feature information of small objects. To address these issues, researchers have proposed various improved convolutional network-based algorithms. For example, [PERSON] et al. use a dense connection structure to enrich feature expression capabilities and increase the number and depth of feature fusion structures to improve detection accuracy [16]. YOLOT uses upsampling for feature map transformation, splices the intermediate feature map with the output feature map, and adds the number of detection heads to improve the detection performance of small targets [17]. [PERSON] et al. combined the semantic information into the shallowest feature map for enhancement and fused deeper features to enhance small target detection performance [18]. [PERSON] et al. employed feature fusion techniques. They improved the loss function to enhance the model's focus on small targets [19]. [PERSON] et al. employed deconvolution for feature map transformation and fused low-level and high-level features to further enhance small target detection [20]. Although the aforementioned feature fusion approaches can improve detection accuracy to some extent, they also introduce background noise that obscures the features of small targets. [PERSON] et al. designed fusion coefficients based on empirical knowledge to determine the fusion ratio between shallow and deep features [21]. However, these coefficient values can be subjective and lack generalization. In response to the limitations of existing object detection algorithms in detecting small objects, this paper proposes a novel object detection algorithm called DCN-YOLO, based on fine-grained feature extraction. The main contributions of this research are as follows: (1) A refined feature extraction network based on DCNv2 is proposed. This network effectively captures the shape and location features of the target by introducing a SimAM attention mechanism. It adaptively changes feature weight in 3D space and reduces noise during contextual semantic fusion. This improves the network's ability to extract target features in complex contexts while maintaining a reduced number of parameters.