YOLO network-based drogue recognition method for autonomous aerial refueling

With the development of aerial refueling technology, autonomous aerial refueling(AAR) has become an important technology in the future battlefield, which is a promising prospective and challenging topic. Since the relative position between the receiver and the drogue is important to accomplish the AAR task, a neural network-based image recognition method is proposed to acquire the required information. Firstly, a C language-based YOLO network is used as the initial network, which meets the requirements of the on-board VxWorks system and can be run directly on the hardware. Then, considering the physical characterizes of the drogue, a multi-dimensional anchor box is designed and the network structure is optimized to adapt to the multi-dimensional situations. Finally, to address the problem of results shifts, feature maps with various sizes and the optimized loss function are used to optimize the network, where the pyramid structure suggests the design of feature maps. The experimental results indicate that the presented method can recognize the drogue more accurately and quickly.