Enhanced YOLO11 for tiny object detection based on multi-scale information interaction and fusion in UAV aerial images

Abstract Object detection is a critical task in drone vision perception systems. However, in complex low-altitude environments, the recognition of tiny, dense, and occluded objects presents significant challenges. To overcome these limitations, this paper proposes an enhanced lightweight object detection model, YOLO-BWS, based on the YOLO11 architecture, aiming to improve tiny object detection performance while maintaining computational efficiency for drone vision. First, we introduce a higher-resolution detection head network to the YOLO11 architecture to incorporate finer spatial feature representations. Second, to optimize information exchange across multi-scale features, we integrate a bidirectional feature pyramid network into the neck structure. This network utilizes its learnable feature fusion mechanism and bidirectional path propagation to effectively enhance semantic expression across different layers. In the bounding box regression branch, we employ a more rigorous Wise Intersection over Union loss function to dynamically adjust the backpropagation gradients based on sample quality, improving the stability of localization and accelerating the convergence of the training process. Experimental results on three datasets, COCO, RSOD, and VisDrone, demonstrate that YOLO-BWS significantly outperforms the baseline YOLO11 across all scenarios, while maintaining a computational cost of 11.6 GFLOPs and only 2.7 million parameters. Compared with YOLO11, YOLO-BWS achieves a 2.37% improvement in mAP@0.5 and a 1.81% improvement in mAP@0.5:0.95 on the COCO dataset. Similarly, on the RSOD dataset, YOLO-BWS improves mAP@0.5 by 3.5% and mAP@0.5:0.95 by 1.1%. On the VisDrone dataset, the mAP@0.5 increases by 4.8%, while the mAP@0.5:0.95 improves by 3.0%. Additionally, visual analysis reveals that YOLO-BWS exhibits exceptional object focusing ability, particularly for tiny and occluded objects, and provides more accurate bounding box regression.