Research on remote sensing object detection based on sparse R-CNN
Remote sensing image object detection tasks have applications in fields such as weather forecasting,environmental monitoring,and military applications.However,the numerous small targets,high similarity between classes,and diverse scales make it difficult to extract features.The method based on deep learning has become the mainstream in the field of object detection.Sparse R-CNN is a model with simple structure and good effect,but its direct application to remote sensing images has poor results.This paper introduces a Self-Supervised learning framework and selective query recollection according to the characteristics of remote sensing images to improve the effect of target detection in remote sensing images,improved by approximately 3.8 percentage points on the mAP metrics.
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