Remote Sensing Image Target Detection Method Based on Refined Feature Extraction

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.