A CenterNet++ model for ship detection in SAR images. (April 2021)

• Record Type: Journal Article
• Authors: Guo, Haoyuan; Yang, Xi; Wang, Nannan; Gao, Xinbo
• Is Part Of: Pattern recognition. Volume 112(2021)
• Publication Date: 2021-04
• Subjects: Ship detection -- Synthetic aperture radar (SAR) -- Deep learning; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107787
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose an effective and stable single-stage detector in a refined manner, achieving high accuracy for small ship detection. We design a feature pyramids fusion module and a head enhancement module to improve ship detection performance under complex background. We achieve favorable results on different SAR image datasets, demonstrating the effectiveness and robustness of our method. Abstract: Ship detection in SAR images is a challenging task due to two difficulties. (1) Because of the long observation distance, ships in SAR images are small with low resolution, leading to high false negative. (2) Because of the complex onshore background, ships are easily confused with other objects with similar appearance. To solve these problems, we propose an effective and stable single-stage detector called CenterNet++. Our model mainly consists of three modules, i.e., feature refinement module, feature pyramids fusion module, and head enhancement module. Firstly, to address small objects detection problem, we design a feature refinement module for extracting multi-scale contextual information. Secondly, feature pyramids fusion module is developed for generating more powerful semantic information. Finally, to alleviate the impact of complex background, head enhancement module is proposed for a balance between foreground and background. To prove the effectiveness and robustness of the proposed method, we make extensive experiments on three popular SAR image datasets, i.e., AIR-SARShip, SSDD, SAR-Ship. The experimental results show that our CenterNet++ reaches state-of-the-art performance on all datasets. In addition, compared with the baseline CenterNet, the proposed method achieves a remarkable accuracy improvement with negligible increase in time cost.