An Antijamming and Lightweight Ship Detector Designed for Spaceborne Optical Images

Abstract

Ship detection in spaceborne optical images is a challenging task because ships have various orientations and scales, especially complex backgrounds, i.e., ships are easily obscured by various jamming. Moreover, most object detectors have enormous computation and parameter numbers, which are unsuitable for resource-bounded spaceborne platforms that contain restrictive memory access and computation. In this article, in order to mitigate the influence of complex backgrounds and jamming on detection, and improve the practicality of detection algorithms, a new satellite optical image dataset and a novel ship detector are proposed. We have collected a new dataset from the satellite, which contains images of different time periods, different illuminations, and different levels of jamming. The proposed dataset is different from the widely used public remote sensing datasets, it is more practical and challenging. The proposed ship detector can deal with various images well and is robust to various complex backgrounds. Specifically, a feature refining module is designed to extract features effectively, which can improve detection performance significantly. An antijamming module is proposed to highlight the features of objects in the whole feature map. In contrast to mainstream ship detectors, the proposed method is effective and lightweight. It can also predict objects with oriented bounding boxes. Moreover, due to the lightweight and simple network design, the designed detector can be easily embedded into edge devices. Extensive experiments demonstrate that the proposed detector is efficient and robust to various complex backgrounds, and the new dataset is more suitable for application scenarios and is quite challenging.

Keywords

• Object detection
• remote sensing satellite imagery
• robust detection
• ship detection
• spaceborne ship dataset