关键词: 遥感图像, 细粒度目标检测, 特征融合, 注意力机制, 正交损失

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 remains a challenge. Among current deep learning-based object detection methods, various improvements targeting different components of models can enhance detection accuracy between fine-grained categories to some extent. However, existing approaches overlook the importance of multi-scale discriminative features and inter-class separation constraints in fine-grained tasks, potentially limiting model performance from feature representation to feature discrimination. To address this issue, this paper proposes a Hierarchical and Orthogonal Fine-Grained Detection Network(HOFD-Net). The model effectively fuses multi-scale features from different levels using a gated fusion mechanism, enhances the representation capability of discriminative features under attention mechanisms with diverse receptive fields, and incorporates 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 remote sensing fine-grained object detection datasets: MAR20 and SMID. Experimental results demonstrate that the proposed model achieves a mean average precision (mAP) of up to 61.45% on the MAR20 dataset, representing an improvement of at least 0.43% and up to 6.29% over the baseline model. On the SMID dataset, it achieves an mAP of up to 63.9%, surpassing the baseline model by a minimum of 1.7%. Furthermore, the model achieves the highest accuracy and performance on both datasets compared to other mainstream algorithms.

Key words: remote sensing images, fine-grained object detection, feature fusion, attention mechanism, orthogonal loss