基于实时图像分割和轨迹预测的空中加油甩鞭特情识别

doi:10.3778/j.issn.1002-8331.2405-0418

摘要/Abstract

摘要： 空中加油对接阶段突发的软管甩鞭特情是威胁飞行安全的主要因素，现有关于甩鞭特情的研究多从物理建模角度进行，而在视觉领域存在研究空白。分析甩鞭现象的时空域特征后，提出了一种基于视觉的特情识别算法，包括软管分割、轨迹预测、多判据判别三个步骤，并搭建了一套预警系统，提高了空中加油任务的安全性。为了兼顾分割的速度和准确度，提出了一种实时分割网络，通过引入自适应区域的自注意力机制，能够快速准确地分割出软管区域，mIoU取得了82.6%的结果，优于现有的实时分割算法；设计了一种相对距离的损失函数，用于训练长短时记忆网络预测软管轨迹；在不同环境下验证了软管分割和轨迹预测算法的有效性，在仿真环境中进行了基于单目视觉引导的甩鞭特情识别实验，使用多重判据实现了对软管甩鞭特情的判别告警，在晴朗的天气环境下达到了81.6%的识别成功率。

关键词: 空中加油, 实时图像分割, 甩鞭特情（HWP）, 轨迹预测

Abstract: Hose whipping phenomenon (HWP) poses a threat to flight safety during aerial refueling docking. Existing research focuses on physical modeling, while there is a research gap in the visual domain. After analyzing the spatiotemporal characteristics of HWP, a vision-based HWP emergency identification algorithm is proposed, which includes three steps：hose segmentation, trajectory prediction, and multi-criteria discrimination. A warning system is developed to enhance the safety of aerial refueling missions. To balance segmentation speed and accuracy, a real-time segmentation network is proposed. By introducing an adaptive regions self-attention mechanism, the network can effectively and accurately segment the hose area, achieving mIoU of 82.6%, which surpasses most existing real-time segmentation algorithms. A relative distance loss function is designed to train long short-term memory network for predicting the hose trajectory. The effectiveness of the hose segmentation and trajectory prediction algorithms is validated in different environments. Hose whipping phenomenon identification experiments based on monocular vision guidance are conducted in a simulation environment. Multiple criteria are used to achieve discrimination and warning of HWP emergencies, achieving an 81.6% success rate under clear weather conditions.

Key words: aerial refueling, real-time image segmentation, hose whipping phenomenon (HWP), trajectory prediction

曾博涵, 郄镕凯, 王璇, 张兆祥, 许悦雷, 康梦特. 基于实时图像分割和轨迹预测的空中加油甩鞭特情识别[J]. 计算机工程与应用, 2025, 61(19): 359-370.

ZENG Bohan, QIE Rongkai, WANG Xuan, ZHANG Zhaoxiang, XU Yuelei, KANG Mengte. Identification of Hose Whipping Phenomenon in Aerial Refueling Based on Real-Time Image Segmentation and Trajectory Prediction[J]. Computer Engineering and Applications, 2025, 61(19): 359-370.

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