基于Faster R-CNN算法的自主空中加油锥套识别

Automatic Identification of Aerial Refueling Cone Sleeve Based on Faster R-CNN Algorithm

随着无人机技术的发展,自主空中加油技术增加了无人机飞行半径和有效载荷,提升了无人机的作战效能。本文针对复杂环境下无人机软管式空中加油的精确引导技术,在无人机自主空中加油近距离对接阶段过程中,对油机锥套识别这个关键环节展开研究。利用深度学习和图形处理,提出一种基于Caffe框架的Faster R-CNN神经网络深度学习的新方法。为了保证该方法的鲁棒性和广泛应用,采用软管式空中加油的真实数据,制作了一个图像的深度学习数据集。根据实验数据验证了基于Caffe框架的Faster R-CNN锥套识别算法的鲁棒性和识别精度,并通过对比实验证明了在复杂的无人机加受油环境中,该识别算法也具有较好的锥套识别能力。

With the development of UAV technology,autonomous aerial refueling technology increases the flight radius and payload of UAV and improves the combat effectiveness of UAV.The paper focuses on the precise guidance technology of UAV hose aerial refueling in complex environment,and studies the key link of drogue detection during the close docking phase of UAV autonomous aerial refueling.Using deep learning and graphics processing unit,a new method based on Faster R-CNN neural network is proposed.In order to ensure its robustness and wide application,an image deep learning data set was made by using real data of hose aerial refueling.Based on the experimental data,the robustness and identification accuracy of the identification algorithm based on Caffe framework Faster R-CNN cone sleeve were verified,and the comparison experiment proved that the identification algorithm also had better identification ability of cone sleeve in the complex UAV oil-feeding environment.