A Dual-Branch Network for End-to-End Point-Supervised Object Detection on Remote Sensing Images

Learning object detectors for remote sensing images commonly requires for a huge number of annotated boundary boxes, which are not available without enormous manual efforts in annotating. Alternatively, points can indicate the existence of the objects of interests with reduced labeling cost. Existing Point-supervised object detection (PSOD) methods predominantly employ a two-stage training strategy, which involves propagating point annotations to pseudo boxes at the first stage then training an object detector with these pseudo boxes in a fully supervised manner. However, such paradigm substantially impedes the end-to-end flow of training gradients. In this work, we propose a novel dual-branch network (DBNet) for end-to-end weakly supervised object detection on remote sensing images. Firstly, a pseudo box generation network is attached to the object detector as a sibling branch, which produces semantic response maps for the objects of interest then extracts pseudo boxes by examining their spatial connectivity. Then, instead of training this pseudo box generation network separately, we jointly adjust the pseudo box generation network and the detection network through a multi-task loss. Experimental results on the DOTA-v1.0 dataset demonstrate the effectiveness of our proposed method, achieving an average precision (mAP
50
) of 32.3%.