Semi-Supervised Image Domain Adaption for Aerial Refueling Drogue Detection on Embedded Chip Under Foggy Conditions

Abstract

The application of aerial refueling technology to UAVs can reduce the dependence on the pilot’s operation, which has unique advantages in carrying out battlefield reconnaissance, monitoring suspicious targets and collecting intelligence through all-weather work. The existing vision-based drogue detection methods are assumed to be carried out under daily lighting conditions, but special weather, such as fog, makes it difficult to identify the characteristics of the drogue, which will greatly degrade the model performance or even fail. Moreover, the traditional computing architecture is difficult to be directly applied to real airborne equipment, so it is necessary to adopt AI processor module with faster and better computing power and supporting parallel computing to meet the requirements of low delay and high security in aerial refueling. Therefore, this work proposes a robust detection network based on image domain adaption. Firstly, an end-to-end image defogging module is designed to deal with foggy image enhancement under weak supervision. Then, knowledge distillation with the teacher-student network is applied to guide the student model to obtain the instance-level features of the unlabeled target domain. In addition, the detection model is compiled and transplanted on the system-on-chip chip of JFMQL100TAI. The comprehensive experimental results on public datasets and real refueling datasets validate the effectiveness and feasibility of the proposed work, which effectively complements the drogue detection of special autonomous aerial refueling tasks. Note to Practitioners—As a widely used refueling technology in the field of national defense, probe-and-drogue refueling has developed from manual control docking to monitoring auxiliary docking. However, it is difficult for pilots to accurately and quickly obtain the relative position of the refueling drogue through visual perception. In this work, a semi-supervised drogue detection network for special aerial refueling task is designed. The proposed work has good application potential in refueling scenes, which can provide fast and accurate drogue positioning under foggy conditions.