Deform-FPN: A Novel FPN with Deformable Convolution for Multi-Scale SAR Ship Detection
Zhang Tianwen Zhang, Xiaoling Zhang, Zikang Shao · 2023
Ship detection from Synthetic Aperture Radar (SAR) images is of great importance. However, the diversity of ship target scales increases the difficulty of detection. To solve this problem, we propose a novel FPN which is enhanced by de-formable convo-lution, called Deform-FPN. Deformable convolution realizes multi-scale adaptive geometric deformation modeling of ships, and can extract multi-scale features of ships with strong ex-pression ability. The multi-level deformable convolution layers enhance the feature extraction and feature fusion capabilities. Specifically, we add deformable convolution to the backbone and lateral connection of Deform-FPN to improve the feature extraction ability. Experimental results on the SAR ship detec-tion dataset (SSDD) reveal the state-of-the-art performance of Deform-FPN, in contrast to other methods based on convolu-tional neural network (CNN). The experimental results show that Deform-FPN offers a 56.5% mAP that is superior to the suboptimal model DCN by 1.5%. In addition, we conducted ablation experiments to verify the effectiveness of the structure of the Deform-FPN we proposed.