航空学报2026，Vol.47Issue(12)：255-273,19.DOI:10.7527/S1000-6893.2025.32898
基于深度集成学习的机载视觉感知鲁棒性设计与分析
Robustness design and analysis of airborne visual perception based on deep ensemble learning
摘要
Abstract
The visual perception function based on machine learning is crucial for enhancing situational awareness or autonomous flight capabilities of aircraft in complex environments,and its performance has significant impact on flight safety.However,the inherent probabilistic nature of machine learning techniques poses substantial challenges to meeting airworthiness safety objectives,thereby hindering their application in airborne systems.To address this issue,a robustness-oriented design method for airborne visual perception based on deep ensemble learning is established.First,a highly representative dataset is generated based on the operational design domain,and a K-fold cross-validation method based on CW-SSIM is proposed to improve the independence between the training and validation sets with limited data.Second,based on the YOLO architecture,depthwise separable convolution is introduced,and three optimized base learners are designed to address different detection needs through multi-scale feature fusion,en-hanced focus on small object detection,and fine-grained feature extraction.Finally,an ensemble learning method is designed using a weighted adaptive fusion strategy to dynamically adjust the weights of base learners,thereby improv-ing the model accuracy and robustness.Experimental results show that the ensemble learning model outperforms de-tection box fusion algorithms such as NMS and WBF.When the IoU is not less than 0.7,the ensemble model im-proves the average P-value,R-value,and F1 score by at least 11.36%,2.06%,and 6.78%,respectively,com-pared to a single model.When the IoU is no less than 0.75,the AP value increases by at least approximately 3%.These results indicate that proposed method significantly enhances target detection accuracy and robustness in com-plex environments,effectively reducing false positives and missed detections,and provides technical assurance for the safe flight of aircraft.关键词
机载视觉感知/鲁棒性设计/深度集成学习/K折交叉验证方法/深度可分离卷积Key words
airborne visual perception/robustness design/deep ensemble learning/K-fold cross-validation method/depthwise separable convolutions分类
航空航天引用本文复制引用
马赞,张同杰,白杰,陈勇,田毅..基于深度集成学习的机载视觉感知鲁棒性设计与分析[J].航空学报,2026,47(12):255-273,19.基金项目
国家重点研发计划(2022YFB3904300) National Key Research and Development Program of China(2022YFB3904300) （2022YFB3904300）
