RA[formula omitted]DC-Net:A residual augment-convolutions and adaptive deformable convolution for points-based anchor-free orientation detection network in remote sensing images

Abstract

With the rapid advancements in computer vision object detection networks, several detection networks based on remote sensing data have been proposed for oriented object detection. However, existing oriented networks have limitations in terms of feature extraction and oriented information extraction. To address these limitations, in this paper, we proposed an advanced adaptive points-based anchor-free oriented detection network called RA2DC-Net which includes a residual augment-convolutions (Res-AugConvs) module that uses residual structures and attention mechanisms to enhance feature focus information and minimize the loss of focus feature information. In addition, we proposed a method called adaptive deformable convolution (ADConv) to fine-tune custom convolution weights according to the features and generate high-quality adaptive offsets for accurate orientation information. Furthermore, we evaluated the performance of RA2DC-Net on two large-scale datasets: the Dataset for Object deTection in Aerial images (DOTA), the rotated object DetectIon in Optical Remote sensing images (DIOR-R) and the High-Resolution Ship Collection 2016 (HRSC2016). Experimental results demonstrated that RA2DC-Net achieved mAP values of 76.25%, 68.73% and 89.75% on the three datasets, respectively, with high AP values for various object classes on DOTA.