Enhancing small object detection in UAV aerial imagery through integration of global edge information and multi-scale feature enhancement

Abstract Object detection in unmanned aerial vehicle (UAV) imagery has significant application value in various fields, including traffic monitoring, disaster rescue, agricultural surveys, and military reconnaissance. However, the variable flight altitudes and perspectives of UAV platforms often results in targets occupying an extremely small pixel proportion, exhibiting limited appearance characteristics, and being susceptible to complex background interference. These factors pose considerable challenges for small object detection. To address these challenges, this paper proposes a novel small object detection framework called CGD-YOLO, based on YOLOv11n. The framework introduces a C3k2-ConvFormer module into the backbone network, which uses depthwise SepConv for efficient spatial feature interaction and a dual amplitude modulation mechanism to alleviate gradient vanishing. Furthermore, a global edge information enhancement module is designed by embedding an edge extractor in shallow layers and enabling cross-layer propagation of edge features, improving the capture of fine contours of small objects. In addition, a multi-dimensional attention mechanism integrating scale, spatial, and task awareness is incorporated into the detection head, substantially enhancing its adaptability and discriminative performance for objects of various sizes and spatial distributions. Moreover, an improved Inner-PIoUv2 loss function is proposed, which integrates a target-size penalty term with a scale factor adjustment strategy to guide anchor box regression more accurately and optimize detection performance across different object scales. Extensive experiments on the VisDrone2019 dataset demonstrate that the proposed method outperforms existing state-of-the-art approaches. It achieves a mean average precision of 31.5%, which is 4.2% higher than the baseline, with notable gains in small object detection, while maintaining real-time inference speed. This study not only provides an efficient and robust solution for small object detection in UAV imagery but also offers valuable insights for enhancing tiny object detection in other visual domains through its global edge feature enhancement strategy.