WFFM-TA-YOLO: A unified weighted feature fusion module with P2-Aware multi-branch structure for remote sensing small object detection

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Small object detection in remote sensing imagery remains challenging due to extremely limited object pixels, dense target distributions, and strong structured background clutter (e.g., roads, rooftops, and shadows). Moreover, many practical airborne and spaceborne platforms impose strict constraints on computation and power consumption, making it difficult to improve detection accuracy without increasing model complexity. To address these challenges, we propose WFFM-TA-YOLO, a lightweight one-stage detector built upon a YOLO-style baseline and tailored for remote sensing small-object detection. The core contribution is a Weighted Feature Fusion Module (WFFM) that integrates a P2-aware multi-branch fusion structure with adaptive scale-wise weighting. By preserving high-resolution P2 features and learning data-driven fusion weights from global feature descriptors, WFFM enhances fine-grained spatial cues while adaptively balancing shallow detail and deep semantic context across aerial scenes. A lightweight two-branch Triplet Attention module is incorporated at the shallow fusion output to suppress repetitive and directional background textures with negligible computational overhead. Extensive experiments on the VEDAI and AI-TOD datasets demonstrate that WFFM-TA-YOLO consistently outperforms the FFCA-YOLO baseline in terms of mAP@0.5, mAP@0.5:0.95, and small-object metrics. Ablation studies confirm that the WFFM and shallow attention components contribute complementary performance gains while maintaining a lightweight and deployable architecture.