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; 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 Clearer the Objects against to the background, the more Confident the Observers 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 paper. First, the multi-frame 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 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.