Abstract: Object detection in remote sensing images is a critical issue in the field of object detection. Currently, most object detection models that using deep learning add attention mechanism during the unidirectional feature fusion process, enhancing various types of objects indiscriminately and failing to highlight small objects. In order to achieve better detection results, an asymmetric high and low-level modulation mechanism is introduced, constructing feature maps that consider shallow detail information and advanced semantic information with the aim of enhancing the characteristics of small objects. Additionally, the DIoU loss function is used instead of the original SmoothL1 loss function to improve model detection accuracy and convergence speed. Furthermore, flexible context information is introduced into in the region of interest classification task to improve the accuracy of small objects classification. Experiments demonstrate that the proposed method achieves good performance on DIOR and NWPU VHR-10 datasets. 

Key words: deep learning, small object detection, remote sensing image, asymmetric high-low layer modulation, context information