AMFC-DEIM: Improved DEIM With Adaptive Matching and Focal Convolution for Remote Sensing Small Object Detection

While Convolutional Neural Network (CNN)-based methods for small object detection in remote sensing imagery have advanced considerably, substantial challenges remain unresolved, primarily stemming from complex backgrounds and insufficient feature representation. To address these issues, we propose a novel architecture specifically designed to accommodate the unique demands of small objects, termed AMFC-DEIM. This framework introduces three key innovations: (1) the Adaptive One-to-One (O2O) matching mechanism, which enhances Dense O2O matching by adaptively adjusting the matching grid configuration to the object distribution, thereby preserving the resolution of small objects throughout training; (2) the Focal Convolution Module, engineered to explicitly align with the spatial characteristics of small objects for extracting fine-grained features; and (3) the Enhanced Normalized Wasserstein Distance, which stabilizes the training process and bolsters performance on small targets. Comprehensive experiments conducted on three benchmark remote sensing small object detection datasets: RSOD, LEVIR-SHIP and NWPU VHR-10, demonstrate that AMFC-DEIM achieves remarkable performance, attaining AP$_{50}$scores of 96.2%, 86.2%, and 95.1%, respectively, while maintaining only 5.27M parameters. These results substantially outperform several established benchmark models and state-of-the-art methods.