Remote Sensing Object Detection Based on Strong Feature Extraction and Prescreening Network

Remote sensing object detection has been an important and challenging research hot spot in computer vision that is widely used in military and civilian fields. Recently, the combined detection model of convolutional neural network (CNN) and transformer has achieved good results, but the problem of poor detection performance of small objects still needs to be solved urgently. This letter proposes a deformable end-to-end object detection with transformers (DETR)-based framework for object detection in remote sensing images. First, multiscale split attention (MSSA) is designed to extract more detailed feature information by grouping. Next, we propose multiscale deformable prescreening attention (MSDPA) mechanism in decoding layer, which achieves the purpose of prescreening, so that the encoder–decoder structure can obtain attention map more efficiently. Finally, the A–D loss function is applied to the prediction layer, increasing the attention of small objects and optimizing the intersection over union (IOU) function. We conduct extensive experiments on the DOTA v1.5 dataset and the HRRSD dataset, which show that the reconstructed detection model is more suitable for remote sensing objects, especially for small objects. The average detection accuracy in DOTA dataset has improved by 4.4% (up to 75.6%), especially the accuracy of small objects has raised by 5%.