Vehicle Detection in UAV Images via Background Suppression Pyramid Network and Multi-Scale Task Adaptive Decoupled Head

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Vehicle detection based on unmanned aerial vehicle (UAV) aerial images plays a significant role in areas such as traffic monitoring and management, disaster relief, and more, garnering extensive attention from researchers in recent years. However, datasets acquired from UAV platforms inevitably suffer from issues such as imbalanced class distribution, severe background interference, numerous small objects, and significant target scale variance, presenting substantial challenges to practical vehicle detection applications based on this platform. Addressing these challenges, this paper proposes an object detection model grounded in a background suppression pyramid network and multi-scale task adaptive decoupled head. Firstly, the model implements a long-tail feature resampling algorithm (LFRA) to solve the problem of imbalanced class distribution in the dataset. Next, a background suppression pyramid network (BSPN) is integrated into the Neck segment of the model. This network not only reduces the interference of redundant background information but also skillfully extracts features of small target vehicles, enhancing the ability of the model to detect small objects. Lastly, a multi-scale task adaptive decoupled head (MTAD) with varied receptive fields is introduced, enhancing detection accuracy by leveraging multi-scale features and adaptively generating relevant features for classification and detection. Experimental results indicate that the proposed model achieves state-of-the-art performance on lightweight object detection networks. Compared to the baseline model PP-YOLOE-s, our model improves the AP50:95 on the VisDrone-Vehicle dataset by 1.9%.