Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (24): 144-151.DOI: 10.3778/j.issn.1002-8331.2102-0270

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Improved FCOS Two-Stage SAR Ship Detection Algorithm

LIU Jingsheng, WU Xing, WANG Honggang, LI Jiangnan   

  1. College of Computer Science, Chongqing University, Chongqing 400044, China
  • Online:2021-12-15 Published:2021-12-13



  1. 重庆大学 计算机学院,重庆 400044


Recently, the anchor-free object detection algorithms have been gradually applied to SAR ship detection. Among them, the FCOS algorithm eliminates the dependence on the setting of anchor parameters, and is more robust to multi-scale and multi-modal ship detection, but there are still two problems:firstly, the algorithm directly performs pixel-by-pixel regression, and the large search space and the difficulty of target regression leads to inaccurate detection. secondly, the insufficient use of low-level features by the feature pyramid leads to amounts of small targets missed. To solve these problems, improvements are made based on FCOS. A two-stage anchor-free detection algorithm is constructed by adding a feature enhancement network. As the first stage, the network provides refined guidance for the detection process, meanwhile enhances the ship feature expression ability. It improves the feature pyramid by introducing more features and adding skip connections to improve the utilization of low-level features. Experiments on datasets SSDD and SAR-Ship-Dataset demonstrate that the mean Average Precision(mAP) is 9.5 and 3.4 percentage points higher than FCOS, and 3.6 and 1.0 percentage points higher than other popular ship detection algorithms respectively, which fully verifies the effectiveness of the proposed algorithm.

Key words: Synthetic Aperture Radar(SAR), ship detection, Fully Convolutional One-Stage object detector(FCOS), two-stage, feature enhancement



关键词: 合成孔径雷达, 舰船检测, FCOS算法, 二阶段, 特征增强