航空学报2025，Vol.46Issue(11)：224-238,15.DOI:10.7527/S1000-6893.2024.31262
铁路外部环境无人机图像未知风险检测方法
Unknown risk detection in external environment of railroad using UAV images
摘要
Abstract
Common hazards as well as unknown risks(including mudslides,rockfalls,animal intrusion,etc.)in the external environment of railroad seriously threaten the safe operation of railroads,requiring frequent time-consuming and laborious inspections by inspectors,but the scope of inspections is still very limited.At present,the low-altitude economy has become China's new quality productivity representative,and the UAV has innate inspection advantages of high altitude,long distance,and small impact from the terrain and railroad maintenance windows.To overcome the challenge of sparse samples and random uncertainty of unknown risks in the external environment of the railroad,this paper utilizes UAVs for remote sensing image acquisition along the railroad,and proposes an unknown risk detection framework based on Faster R-CNN.Firstly,a novel targeted and multi-classification decoupling training strategy is de-signed and integrated in the unknown risk detection framework,which significantly improves the performance of gen-eral object detection and avoids misclassifying unknown risk objects as background.Secondly,the virtual feature syn-thesis method of VOS(Visual Object Segmentation)is improved,and similarity-based feature space sampling is de-signed to obtain a generalized unknown risk object feature representation by performing multivariate Gaussian distribu-tion parameter estimation and resampling based on the construction of instance-level object feature space.Subse-quently,an energy-based uncertainty measurement is utilized to measure the uncertainty of instance-level features,and losses are calculated accordingly to induce the network to optimize the decision boundaries for common and un-known risk categories.Finally,quantitative and qualitative experimental analyses are conducted on the collected rail-road external environment dataset,open-source drone dataset,and generalization test data.The proposed method achieves 95.7%mAP50 in common hazard identification,while achieving 98%and 80.8%Recall50 in the test set and generalization test data,respectively.The experimental results show that the proposed method has high detection ability for unknown risk objects,while ensuring high recognition rate of common hazard categories.关键词
无人机/遥感图像/低空经济/深度学习/铁路外部环境/未知风险目标检测Key words
UAV/remote sensing imagery/low-altitude economy/deep learning/external environment of railroad/unknown risk object detection分类
航空航天引用本文复制引用
孟凡腾,秦勇,崔京,吴云鹏,张紫城,魏少伟..铁路外部环境无人机图像未知风险检测方法[J].航空学报,2025,46(11):224-238,15.基金项目
中央高校基本科研业务费专项(2024QYBS033) （2024QYBS033）
国家重点研发计划(2022YFB4300600) the Fundamental Research Funds for the Central Universities(2024QYBS033) （2022YFB4300600）
National Key Research and Development Program of China(2022YFB4300600) （2022YFB4300600）
