遥感图像飞机细粒度目标检测算法OA

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.