Lightweight Context Awareness and Feature Enhancement for Anchor-Free Remote- Sensing Target Detection

Optical remote-sensing image target detection holds significant research significance in various domains, including disaster relief, ecological environment protection, and military surveillance. However, since remote-sensing images have multiscale targets, complex backgrounds, and many small targets, the performance of the existing network models in remote-sensing image target detection cannot reach what we expect. In addition, we note that current networks use complex computational mechanisms that make the models time-consuming, which hinders their practicability in remote-sensing target detection scenarios. In response to this challenge, we propose an anchor-free and efficient one-stage target detection method for optical remote-sensing images. First, we propose the lightweight context-aware module GSelf-Attention, injected into the feature fusion network from top-to-bottom and bottom-to-top to enhance the feature information interaction. Second, we proposed that ELAN-RSN uses an optimized residual shrinkage network (RSN) to eliminate background noise and conflicting information in the multiscale feature fusion. Finally, we introduce the decoupled head fused with SPDConv to enhance the detection accuracy of small target objects further. The performance of the proposed algorithm is compared with that of other advanced methods on DIOR and RSOD datasets. The experimental results show that the proposed algorithm significantly improves object detection accuracy while ensuring detection efficiency and has high robustness. The code is available at
https://github.com/FF-codeHouse/Object-Detection/tree/remote-sensing
.