Deep Adaptive Proposal Network for Object Detection in Optical Remote Sensing Images
This work addresses the challenge of varying object densities in remote sensing images for aerial and satellite image analysis, representing an incremental improvement over existing two-stage detectors like Faster R-CNN.
The paper tackles the problem of object detection in optical remote sensing images, where objects have sparse and dense characteristics, by proposing a deep adaptive proposal network (DAPNet) that adapts region proposals based on object categories and numbers, achieving state-of-the-art performance on the NWPU VHR-10 dataset.
Object detection is a fundamental and challenging problem in aerial and satellite image analysis. More recently, a two-stage detector Faster R-CNN is proposed and demonstrated to be a promising tool for object detection in optical remote sensing images, while the sparse and dense characteristic of objects in remote sensing images is complexity. It is unreasonable to treat all images with the same region proposal strategy, and this treatment limits the performance of two-stage detectors. In this paper, we propose a novel and effective approach, named deep adaptive proposal network (DAPNet), address this complexity characteristic of object by learning a new category prior network (CPN) on the basis of the existing Faster R-CNN architecture. Moreover, the candidate regions produced by DAPNet model are different from the traditional region proposal network (RPN), DAPNet predicts the detail category of each candidate region. And these candidate regions combine the object number, which generated by the category prior network to achieve a suitable number of candidate boxes for each image. These candidate boxes can satisfy detection tasks in sparse and dense scenes. The performance of the proposed framework has been evaluated on the challenging NWPU VHR-10 data set. Experimental results demonstrate the superiority of the proposed framework to the state-of-the-art.