Boosting UAV Detection via Memory-Enhanced Attention and Contrastive Learning

With unmanned aerial vehicles (UAVs) having emerged in diverse application domains, visual detection of UAVs has become a critical research focus in recent years. However, most existing methods are limited in capturing small UAVs and may not perform well in complex backgrounds. To address these challenges, we propose a novel detection framework that integrates newly designed memory mechanism and contrastive loss to improve UAV detection. Specifically, we first utilize a clustering algorithm to gather representative UAV prototypes, which are then utilized to construct a reliable memory bank. Then, we design a UAV Memory-Enhanced Attention (UMEA) module to propagate high-confidence prototypes from the memory bank, thereby enhancing the appearance features of UAVs in the input frame. Furthermore, we introduce a Memory-Driven Contrastive Learning (MDCL) loss function to pull UAVs closer in the feature space while pushing them further away from the background. Extensive experiments conducted on three challenging datasets, NPS-Drones, ARD-MAV and Drone-vs-Bird demonstrate that the proposed method outperforms several state-of-the-art models in terms of the main metric AP with a large absolute margin, 2.1%, 3.6%, and 4.4%, respectively.