COCO-Net: A Dual-Supervised Network With Unified ROI-Loss for Low-Resolution Ship Detection From Optical Satellite Image Sequences

Qizhi Xu, Yuan Li, Mingjin Zhang, Wei Li

Abstract: Low-resolution ship detection from optical satellite image sequences is critical in high-orbit remote sensing satellite applications. However, it is still a difficult problem due to the following challenges: 1) the size of the ship is tiny in the low-resolution image; 2) the ship target is dim and the contrast with the background is low; and 3) the interference of cloud and fog covering is complex and changeable. For these reasons, the targets are easily lost during the detection. In fact, the C learer the O bjects against to the background, the more C onfident the O bservers can detect it. In light of these considerations, we propose a COCO-Net to detect the small dynamic objects on low-resolution images in this article. First, the multiframe images are associated by introducing motion information as an effective compensation for small object features. Second, an integrated dual-supervised network that processes single-level tasks hierarchically is presented to adaptively enhance the input data quality of object detection without being limited by diverse scene disturbances. Third, a unified region of interest (ROI)-loss scheme that modulates the loss function of the first component by introducing ROI-masks from the second component is utilized to make the first component also work for object detection. In addition, we construct a new dataset for the small dynamic object detection based on the GaoFen-4 satellite imagery. Comprehensive experiments on a self-assembled dataset from the GaoFen-4 satellite show the superior performance of the proposed method compared to state-of-the-art object detectors.