RS-FeatFuseNet: An Integrated Remote Sensing Object Detection Model with Enhanced Feature Extraction

With the advancement of satellite and sensor technologies, remote sensing images are playing crucial roles in both civilian and military domains. This paper addresses challenges such as complex backgrounds and scale variations in remote sensing images by proposing a novel attention mechanism called ESHA. This mechanism effectively integrates multi-scale feature information and introduces a multi-head self-attention (MHSA) to better capture contextual information surrounding objects, enhancing the model’s ability to perceive complex scenes. Additionally, we optimized the C2f module of YOLOv8, which enhances the model’s representational capacity by introducing a parallel multi-branch structure to learn features at different levels, resolving feature scarcity issues. During training, we utilized focal loss to handle the issue of imbalanced target class distributions in remote sensing datasets, improving the detection accuracy of challenging objects. The final network model achieved training accuracies of 89.1%, 91.6%, and 73.2% on the DIOR, NWPU VHR-10, and VEDAI datasets, respectively.