EBAD-YOLO: Efficient Bidirectional Adaptive Dense Network for UAV Small Object Detection

Abstract Object detection in drone imagery faces substantial challenges in achieving both high accuracy and computational efficiency for on-board deployment. To address these issues, we propose EBAD-YOLO (Efficient Bidirectional Adaptive Dense YOLO), an enhanced architecture built upon YOLOv10. Specifically, we propose a Bidirectional Adaptive Dense Connection Feature Pyramid Network (BADC-FPN) that facilitates effective multi-scale feature fusion through bidirectional cross-scale dense connections and adaptive weighting mechanisms. Building on this, we improve efficiency by optimizing C2f with the Fasterblock module. Additionally, a Localization Quality Estimation (LQE) module is incorporated into the detection head to suppress low-quality predictions, thus optimizing the precision-recall trade-off. Finally, layer-adaptive magnitude-based pruning (LAMP) is employed to further compress the model, ensuring efficient deployment. Evaluations on the VisDrone2019 dataset demonstrate that EBAD-YOLO enhances mAP@50 by 3.6 \% compared to YOLOv10s, while reducing GFLOPs by 56.5\% and the number of parameters by 66.7\%. Its robust generalization capability is further validated on the TinyPerson and LEVIR-Ship datasets.