YOLO-DAA: Directional Area Attention for Lightweight Tiny Object Detection in Maritime UAV Imagery

Tiny object detection in maritime Unmanned Aerial Vehicles (UAV) imagery remains challenging due to low-resolution targets, dynamic lighting, and vast water backgrounds that obscure fine spatial cues. This study introduces You Only Look Once – Directional Area Attention (YOLO-DAA), a lightweight yet direction-aware detection framework designed to enhance spatial reasoning and feature discrimination for maritime environments. The proposed model integrates two key components: the Spatial Reconstruction Unit (SRU), which dynamically filters redundant activations and reconstructs informative spatial features, and the Directional Area Attention (DAA), which introduces controllable row–column attention to model anisotropic dependencies. Together, they enable the network to capture orientation-sensitive structures such as elongated vessels and vertically aligned swimmers while maintaining real-time efficiency. Experimental results on Common Objects in Context (COCO) and SeaDronesSee datasets demonstrate that YOLO-DAA achieves significant improvements in both precision and recall, outperforming the YOLOv12-turbo baseline across multiple scales. In particular, the lightweight YOLO-DAA-n variant achieves a 12.5% AP95 gain on SeaDronesSee with minimal computational overhead. The findings confirm that directional attention and spatial reconstruction jointly enhance the representation of tiny maritime targets, offering an effective balance between accuracy and efficiency for real-world UAV deployments.