航空学报2026，Vol.47Issue(10)：109-123,15.DOI:10.7527/S1000-6893.2025.32578
遥感图像飞机细粒度目标检测算法
Aircraft fine-grained object detection algorithm in remote sensing images
摘要
Abstract
Aircraft targets in remote sensing images often have characteristics of similar shapes,especially the only slight differences between specific models,so that accurately detecting and recognizing fine-grained aircraft targets re-mains a challenge.Among current deep learning-based object detection methods,various improvements targeting dif-ferent components of models can enhance detection accuracy between fine-grained categories to some extent.How-ever,existing approaches overlook the importance of multi-scale discriminative features and inter-class separation con-straints in fine-grained tasks,potentially limiting model performance from feature representation to feature discrimina-tion.To address this issue,this paper proposes a Hierarchical and Orthogonal Fine-Grained Detection Network.The model effectively fuses multi-scale features from different levels using a gated fusion mechanism,enhances the repre-sentation capability of discriminative features under attention mechanisms with diverse receptive fields,and incorpo-rates adaptive loss term weighting within the orthogonal loss function to strengthen intra-class compactness and inter-class separability of features.Consequently,the model's capability for representing and discriminating fine-grained target features is improved.Comprehensive ablation studies and comparative experiments were conducted on two re-mote sensing fine-grained object detection datasets:MAR20 and SMID.Experimental results demonstrate that the proposed model achieves a mean average precision of up to 61.45%on the MAR20 dataset,representing an im-provement of at least 0.43%and up to 6.29%over the baseline model and a mean average precision of up to 63.9%on the SMID dataset,surpassing the baseline model by a minimum of 1.7%.Across both datasets,the proposed model achieves the highest accuracy and performance compared to other mainstream algorithms.关键词
遥感图像/目标检测/特征融合/注意力机制/正交损失Key words
remote sensing images/object detection/feature fusion/attention mechanism/orthogonal loss分类
航空航天引用本文复制引用
周雷,谷延锋,刘天竹..遥感图像飞机细粒度目标检测算法[J].航空学报,2026,47(10):109-123,15.基金项目
国家重点研发计划重点专项(2024YFF1401002) National Key Research and Development Program of China(2024YFF1401002) （2024YFF1401002）
