DFA-Det: Dynamic Feature Augmentation and Hierarchical Adaptive Fusion for Small Object Detection in Low-Altitude Complex Scenes

Abstract

Low-altitude unmanned-aerial-vehicle imagery exposes object detectors to a distinctive combination of tiny object footprints, dense instance layouts, abrupt scale variation, and weak texture under complex urban backgrounds. Existing detectors usually address these factors by adding larger backbones, denser feature pyramids, or heavier attention, but such independent additions often amplify background responses and dilute the fine localization cues needed by small targets. This paper proposes DFA-Det, a dynamic feature augmentation detector that treats low-altitude small-object detection as a coupled problem of context preservation, scale calibration, and content-aware refinement. The method first introduces a poly-kernel inception enhancement branch to preserve shallow structural details while expanding the effective receptive field. It then builds a Multi-Scale Interaction Encoder with Adaptive Feature Prior Learning and an Adaptive Feature Scaling Layer, where the latter contains a Bi-directional Channel Fusion Module that learns channel-wise evidence exchange between adjacent resolutions. Finally, a Hierarchical Refinement and Adaptive Fusion Module performs dynamic upsampling, semantic refinement, and adaptive fusion before the detection decoder. Experiments on the public VisDrone and CODrone benchmarks show that DFA-Det improves small-object precision, crowded-scene recall, and cross-scale robustness compared with representative two-stage, one-stage, transformer-based, and recent YOLO-family detectors. Extensive ablations, heatmaps, and qualitative comparisons indicate that the proposed modules cooperate as a coherent dynamic feature enhancement mechanism rather than isolated architectural attachments.