摘要： 传统视觉方案无法应对无人机降落过程中复杂的环境变化，难以实现在机载处理器上的实时图像处理。为此，提出一种适用于无人机板载端轻量高效的Onboard-YOLO算法，使用可分离卷积代替常规卷积核提升计算速度，通过注意力机制自动学习通道特征权重提高模型准确度。在运动模糊、遮挡、目标出视野、光照、尺度变化等5种干扰环境下进行降落测试，结果表明，Onboard-YOLO可以解决降落过程中的复杂环境问题，在板载端计算速度达到18.3 frame/s，相比于原始YOLO算法、Faster-RCNN算法分别提升了4.3倍、25.7倍，其算法平均准确度达到0.91，相比SSD-Mobilenet提高了8.9个百分点。经实际测试验证了该算法可实现无人机板载端的实时自主精准降落，达到95%以上的降落成功率。

关键词: 无人机, 精准降落, 深度学习, 目标检测, 注意力机制

Abstract: Traditional vision-based landing schemes cannot cope with the complicated environmental changes during landing of Unmanned Aerial Vehicles(UAV), and thus fail to process images in real time using UAV-borne processors.To address the problem, a lightweight and efficient Onboard-YOLO algorithm is proposed for UAV-borne processors.The algorithm employs separable convolution instead of conventional convolution kernels to improve the calculation speed.Then the attention mechanism is used for the automatic learning of channel feature weights to improve the accuracy of the model.The landing algorithm is tested in various cases of interference, including motion blur, occlusion, target going beyond the visual field, illumination and scale changes.The test results show that compared with the advanced real-time detection algorithms, the proposed Onboard-YOLO algorithm can deal with the complicated environmental changes better during landing.Its calculation speed reaches 18.3 frames per second on the airborne processor, which is 4.3 times faster than that of the original YOLO algorithm, and 25.7 times faster than that of Faster-RCNN.Additionally, the accuracy of the algorithm reaches 0.91, which is 8.9 percentage points higher than that of Mobilenet-SSD.Onboard-YOLO enables autonomous real-time precise landing based on the airborne processor, bringing the success rate of landing to 95%.

Key words: Unmanned Aerial Vehicles(UAV), precise landing, deep learning, target detection, attention mechanism

中图分类号: 

• TP317.4