UAV-MaLO: Mamba-Augmented YOLO Hybrid Architecture for UAV Micro-Object Detection in Autonomous Robotics

Abstract

The rapid advancement of drone technology has led to the widespread application of micro-object detection in Unmanned Aerial Vehicle (UAV) systems. However, with the constraint of real-time computation, critical challenges remain in addressing extreme scale variations, low-resolution signatures and dense occlusions. For object detection task, although YOLO-based detectors outperform transformer models in efficiency-accuracy balance, their limited capacity for global context modeling and feature discriminability in complex aerial environments hinders optimal performance. To overcome these limitations, we introduce UAV-MaLO, a novel framework that incorporates state space modeling principles into YOLO’s architecture. By introducing the abilities of long-range dependency modeling and adaptive spatial-frequency fusion, the proposed approach dynamically optimizes receptive fields while suppressing background interference, achieving robust micro-object localization in cluttered scenarios. Furthermore, the parallelized attention mechanism and the hierarchical feature refinement further ensure real-time processing capabilities without compromising detection precision, establishing a new paradigm for UAV deployment. Our experimental results on the VisDrone-2019-DET dataset reveal a significant improvement in various variants of average precision (AP), indicating the extraordinary performance of our UAV-MaLO.