Few-shot object detection in optical remote sensing images
Objects in remote sensing images are detected by determining the positions and correct categories of objects.Given that this approach has broad application prospects and plays a vital role in many fields,the purpose of this paper is to study its issues,such as insufficient feature extraction,difficultly in locating objects,and confusing classification,when applied to small samples.The specific contributions of this paper are as follows:(1)A collaborative attention module is proposed,which includes designed background attenuating attention and spatial perception attention.The network focuses on key information related to object positioning on the basis of rich background and object feature information,and an RPN network generates improved regional suggestion boxes,reduces the probability of missing targets,and improves the positioning performance of the model for small-sample categories.(2)A contrastive learning branch is designed.Based on the design of the contrast loss function,feature learning is gradually transferred to classifier learning through a joint training strategy,and classification accuracy is improved.(3)A few-shot object detection model based on the fine-tuning transfer learning paradigm is designed,which is divided into basic training and fine-tuning stages.In the basic training stage,the model is trained to learn class-independent parameters with sufficient base class samples.In the fine-tuning stage,a small sample data set is used to enable the target detection model to adapt to specific objects and improve its detection performance.On the basis of the TFA,this article verifies the effectiveness of the proposed algorithm on the remote sensing data sets NWPU,VHR-10,and DIOR.To demonstrate the superiority of our method to other methods for few-shot object detection,we compared our method with TFA,FR,FSODM,DeFRCN,and MFDC.Results show that the proposed algorithm considerably improved the mean average accuracy on NWPU,VHR-10,and DIOR data sets,demonstrating the superiority of the proposed algorithm over the above algorithms.In summary,our method can achieve exceptional detection results from small remote sensing images,exhibiting effectiveness and superiority over other detection methods.We hope that our method can promote further research into few-shot object detection and contribute to the development of this field.
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