轻量级视觉模型提升无人机着陆偏差检测精度与响应速度
AeroLite-MDNet: Lightweight Multi-task Deviation Detection Network for UAV Landing
- 设计轻量多任务网络，融合多尺度特征与分割分支提升检测鲁棒性
- 在真实场景数据集上实现0.7秒平均预警延迟，偏差检测准确率达98.6%
- 适用于需要高可靠着陆的无人机系统，尤其适合资源受限平台
无人机在测绘、运输和环境监测等任务中广泛应用。任务完成后需精准降落至对接站以实现存储或充电，确保作业连续性。然而，受GPS信号干扰等因素影响，精准着陆仍具挑战。为此，本文提出一种基于视觉的无人机着陆偏差预警系统，核心为新型轻量级多任务检测网络AeroLite-MDNet。该模型引入多尺度融合模块以增强跨尺度目标检测能力，并集成分割分支实现高效姿态估计。我们提出新评估指标平均预警延迟（AWD）以量化系统对偏差的敏感度。此外，构建新数据集UAVLandData，涵盖真实着陆偏差场景，支持训练与评估。实验表明，系统在测试中达到0.7秒的平均预警延迟，偏差检测准确率为98.6%，显著提升无人机着陆可靠性。代码将开源至https://github.com/ITTTTTI/Maskyolo.git。
原文摘要 · Abstract (English)
Unmanned aerial vehicles (UAVs) are increasingly employed in diverse applications such as land surveying, material transport, and environmental monitoring. Following missions like data collection or inspection, UAVs must land safely at docking stations for storage or recharging, which is an essential requirement for ensuring operational continuity. However, accurate landing remains challenging due to factors like GPS signal interference. To address this issue, we propose a deviation warning system for UAV landings, powered by a novel vision-based model called AeroLite-MDNet. This model integrates a multiscale fusion module for robust cross-scale object detection and incorporates a segmentation branch for efficient orientation estimation. We introduce a new evaluation metric, Average Warning Delay (AWD), to quantify the system's sensitivity to landing deviations. Furthermore, we contribute a new dataset, UAVLandData, which captures real-world landing deviation scenarios to support training and evaluation. Experimental results show that our system achieves an AWD of 0.7 seconds with a deviation detection accuracy of 98.6\%, demonstrating its effectiveness in enhancing UAV landing reliability. Code will be available at https://github.com/ITTTTTI/Maskyolo.git
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