| Citation: | LI H G，WANG Y F，YANG L C. Meta-learning-based few-shot object detection for remote sensing images[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（8）：2503-2513 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0637
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This study introduces meta-learning technology to propose a meta-learning-based few-shot object detection algorithm for the few-shot item detection job in remote sensing photos. The object and background are easily confused under the condition of large-scale changes and small samples in remote sensing images. To solve this issue, we expand the single-scale re-weighting into a multi-scale re-weighting module in the feature extraction part, where the prior knowledge of supporting samples can be adapted to different objects. In order to solve the problem of large inter-class similarities and intra-class differences among remote sensing objects, a scene correction module is designed by leveraging the dependence relationship between the object and scene to correct the detected object’s category. In order to restrict the feature distributions of various objects, we additionally incorporate the marginal loss to the feature space. Experimental results show that the proposed algorithm achieves high detection performance on the 10-shot task setting, achieving mean average precision (mAP) of 64.18% and 37.27% on the new category of NWPU VHR-10 and DIOR datasets, respectively.
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