Balanced Feature Pyramid Network for Ship Detection in Synthetic Aperture Radar Images
Tianwen Zhang, Xiaoling Zhang, Jun Shi, Shunjun Wei, Jianguo Wang, Jianwei Li
Abstract
Ship detection in Synthetic Aperture Radar (SAR) images is a fundamental but challenging task. Nowadays, given that the huge imbalance between sparse-distribution ships and complex backgrounds in training process, most existing deep-learning-based SAR ship detectors often face great difficulty in further improving accuracy. Therefore, to solve this problem, in this paper, a novel Balanced Feature Pyramid Network (B-FPN) is applied to enhance detection accuracy. Different from the raw Feature Pyramid Network (FPN), B-FPN utilizes the same-deep integration balanced semantic features to strengthen the multi-level features in the feature pyramid, by means of four steps, namely rescaling, integrating, refining and strengthening, which do not increase too much network parameter quantity. Experimental results on the open SAR Ship Detection Dataset (SSDD) shows that B-FPN can make a 7.15% mean Average Precision (mAP) improvement than FPN.