Focal Spotter: Small-Object Detection via Inner Scale and Interscale Feature Focalization

Small object detection is a challenging but vital task for applications such as search and rescue, surveillance, and remote sensing, where the goal is to detect small targets such as people, vehicles, or boats in complex environments. The minimal pixel representation of such targets, combined with cluttered backgrounds and environmental variations like lighting changes, makes accurate detection particularly difficult. To address these challenges, we propose Focal Spotter, a novel transformer-based detector that incorporates two key modules:the Energy-Based Focal Module (EFM) and the Focalized Inter-Scale Feature Complementary Module (FICM). The EFM leverages an Energy-Based Model(EBM) to achieve inner-scale feature focalization. The EBM dynamically allocates weights through an energy function, focusing on low-energy target signals (such as small object features), significantly enhancing the model’s sensitivity to sparse or weak signals. This capability allows EFM to outperform conventional attention methods in both robustness and precision.The FICM facilitates inter-scale feature focalization by aligning and integrating high-level semantic and low-level detailed features spatially and channel-wise. By preemptively extracting and merging compensatory information across scales, it boosts feature fusion efficiency, minimizes redundancy, ensures semantic consistency, and achieves precise spatial localization, significantly enhancing small object detection in complex scenes. Extensive experiments on multiple benchmark datasets, including SeaDronesSee and VisDrone, demonstrate that Focal Spotter outperforms state-of-the-art methods in small object detection. Ablation studies highlight the critical contributions of EFM and FICM, with EFM showing superior performance over other attention mechanisms in capturing sparse small targets. The results underscore the robustness and effectiveness of our approach across diverse and challenging scenarios, from maritime to urban environments.