Multiscale Deformable Attention and Multilevel Features Aggregation for Remote Sensing Object Detection
Xiaohu Dong, Yao Qin, Ruigang Fu, Yinghui Gao, Songlin Liu, Yuanxin Ye, Biao Li
Abstract
In this letter, a novel object detection method based on feature pyramid network (FPN) is proposed to improve the detection performance of remote sensing objects. First, since the information in the background regions may interfere with object detection, a novel multi-scale deformable attention module (MSDAM) is designed and added on the top of the backbone of FPN to make the network suppress the background features while highlight the target features. The proposed MSDAM generates attention maps from feature maps with multi-scale deformable receptive fields, thus can fit remote sensing objects of various shapes and sizes better and predict more precise attention maps for remote sensing images. Second, in the original FPN, each proposal is predicted based on feature grids pooled from only one feature level. This process is suboptimal as the information discarded in other feature levels and the global contextual information are also meaningful to object detection. Thus, a multi-level features aggregation module (MLFAM) is proposed to aggregate the multi-level outputs of FPN and the global context of the whole image, generating more powerful pyramidal representations for the subsequent object detection. The experiments conducted on the DIOR and RSOD datasets demonstrate the superiority of the proposed method over the considered state-of-the-art baseline methods in terms of detection accuracy.