U-Shaped Feature Extraction and Fusion Network for Object Detection in Low-Altitude UAV Images

Abstract

In the past decade, object detection technology has developed rapidly. However, in the field of unmanned aerial vehicle (UAV) image object detection, challenges such as complex environments, numerous and dense small objects, and weak features make object detection from the UAV perspective a highly challenging task. To address these issues, this letter proposes a U-shaped feature extraction and fusion network (U-ShapeNet). Specifically: first, to enhance the network’s feature extraction capability and improve the perception of small objects, we design a novel U-shaped feature extraction network (U-SFEN) and introduce a tiny object detection head. Second, a large kernel feature selection module (LKFSM) is constructed to strengthen the network’s contextual information learning ability and effectively distinguish small objects from complex background noise. Third, a same-scale feature enhancement module (SFEM) is proposed to mitigate information decay by reusing same-scale feature maps. Experiments on the VisDrone2019 and HazyDet datasets demonstrate that U-ShapeNet outperforms current mainstream object detectors, achieving state-of-the-art performance.