Boosting UAV Detection via Memory-Enhanced Attention and Contrastive Learning
Yuqing Liu, Yunchuan Ma, Yuankai Qi, Laiyun Qing, Guorong Li
Abstract
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.