Regression-Guided Refocusing Learning with Feature Alignment for Remote Sensing Tiny Object Detection

Tiny object detection is a formidable challenge in remote sensing intelligent interpretation. Tiny objects are usually fuzzy, densely distributed, and highly sensitive to positioning errors, which leads to the mainstream detector usually achieving suboptimal detection performance when facing tiny objects. To address the mismatch of mainstream detector architectures and model optimization strategies in the context of tiny object detection, this article presents an efficient and interpretable algorithm for tiny object detection, termed the cross-attention-based feature fusion enhanced tiny object detection network (CAF2ENet). First, the cross-attention mechanism is introduced to refine the upsampling results of deep features. This refinement improves the precision of multiscale feature fusion. Second, a training strategy named regression-based refocusing learning is introduced. Deviating from the conventional optimization strategy, our method guides the optimizer to prioritize higher-quality detection boxes by adjusting sample weights. This adjustment significantly amplifies the detector's potential to achieve superior detection results. Finally, the object composite confidence score is employed for the interpretable filtering of detection boxes. Extensive experiments on tiny object detection in aerial images (AI-TOD) and object detection in optical remote sensing images (DIOR) datasets are carried out, and comparison indicates that the proposed CAF2ENet can perform the remarkable performance compared to other state-of-the-art (SOTA) tiny object detection detectors, as it can reach 63.7% average precision (AP50) on AI-TOD and 75.4% AP50 on DIOR, achieve SOTA performance.