AERIS-ED: A Novel Efficient Attention Riser for Multi-Scale Object Detection in Remote Sensing

Object detection in remote sensing images is still recognized as a demanding task, largely because of significant scale differences among objects and the complexity of background scenes. Detecting small and medium-sized targets within cluttered environments, in particular, continues to challenge many existing algorithms. To address these issues, this study presents a new model named AERIS-ED (Attention-Enhanced Real-time Intelligence System for Efficient Detection). The framework adopts a C3 (Cross Stage Partial with three convolutions) based backbone and incorporates Efficient Attention (EA) units, but unlike conventional designs, these modules are inserted only at the P3 and P4 levels of the feature pyramid. This focused integration enables richer feature interaction across scales and enhances the recognition of small and medium objects. Comprehensive experiments on the MAR20 and VEDAI datasets highlight the benefits of the proposed approach. On MAR20, the model achieves a mean Average Precision at an Intersection over Union threshold of 0.5 (mAP@0.5) of 95.1% with an inference latency of only 3.8 ms/img. On VEDAI, it secures 83.0% mAP@0.5 while maintaining the same efficiency, thereby confirming its suitability for real-time applications. Overall, the results indicate that AERIS-ED strengthens detection accuracy for small objects without compromising computational speed. These improvements suggest that the architecture is not only promising for multi-scale detection research but also has strong potential in practical remote sensing tasks.