SPAF: Enhancing YOLO-Based Object Detection Using Sparse Attention and Progressive Adaptive Fusion

Abstract

Detecting objects in Autonomous Aerial Vehicles (AAVs) imagery remains a challenging task due to large variations in object sizes and complex backgrounds, which often leads to missed detections and reduced accuracy, especially for small objects that contain limited visual information and are easily lost during feature extraction. To address these challenges, this paper proposes SPAF, an enhanced YOLO-based model built upon YOLO26 that improves object detection through the integration of a multi-scale attention mechanism, progressive feature aggregation, and adaptive feature fusion. The proposed architecture enhances the backbone network by integrating a Sparse Vision Attention (SVA) module to facilitate the extraction of fine-grained and task-relevant features in complex aerial scenes. In the neck, the Progressive Bidirectional Auxiliary Pyramid Network (PBAP-Net) facilitates efficient cross-scale feature aggregation by combining top-down semantic information with bottom-up spatial details, thereby strengthening multi-scale representation. Finally, Adaptive Channel Multi-Scale Fusion Head(ACMF-Head) refines the final feature representations by adaptively fusing multi-scale information and reducing inconsistencies across feature levels, resulting in more reliable and robust predictions. Experimental results on the VisDrone2019 dataset demonstrate that SPAF achieves 46.8% mAP@0.5, which is 7.5% higher compared to the baseline model. These results confirm the effectiveness of the proposed approach for robust AAV object detection in challenging aerial environments.