Adaptive Dual-Domain Dynamic Interactive Network for Oriented Object Detection in Remote Sensing Images

Object detection in remote sensing images is an important research topic in the field of remote sensing intelligent interpretation. Although modern object detectors have made good progress, high-precision oriented object detection still faces severe challenges due to the large-scale variation, strong directional diversity and complex background interference of objects in remote sensing images. Currently, most remote sensing object detectors focus on modeling object characteristics in the spatial domain while ignoring the frequency domain information of the object. Recent studies have shown that frequency domain learning has brought substantial benefits in many visual fields. To this end, we proposed an adaptive dual-domain dynamic interaction network (AD3I-Net) for oriented object detection tasks in remote sensing images. The network has three important components: a spatial adaptive selection (SAS) module, a frequency adaptive selection (FAS) module, and a dual-domain feature interaction (DDFI) module. The SAS module adaptively models spatial context information and dynamically adjusts the feature receptive field to construct more accurate spatial position features for objects of different scales. The FAS module uses the transformation from the spatial domain to the frequency domain to adaptively learn the frequency information of the object, to model direction features, and to make up for the lack of spatial domain information. Finally, through the DDFI module, the features extracted from the two domains are interactively fused, thereby bridging the complementary information to enhance the feature expression of the object and give it rich spatial position and direction information. The AD3I-Net we proposed fully exploits the interaction between the different domains and improves the model’s ability to capture subtle object features. Our method has been extensively experimentally verified on two mainstream datasets, HRSC2016 and DIOR-R. The experimental results demonstrate that this method performs competitively in oriented object detection tasks.