Fully Deformable Convolutional Network for Ship Detection in Remote Sensing Imagery

Remote Sensing

Yuman Yuan¹  Hongyang Bai¹  Hongwei Guo¹  Weiwei Qin² 

[1] School of Energy and Power Engineering, Nanjing University of Science and Technology (NJUST), Nanjing 210094, China;Xi’an Research Institute of High-Tech, Xi’an 710025, China;

关键词: remote sensing;    ship detection;    feature pyramid network;    deformable convolution;    convolutional neural networks (CNNs);   

DOI  :  10.3390/rs14081850

来源: DOAJ

【 摘 要 】

In high spatial resolution remote sensing imagery (HRSI), ship detection plays a fundamental role in a wide variety of applications. Despite the remarkable progress made by many methods, ship detection remains challenging due to the dense distribution, the complex background, and the huge differences in scale and orientation of ships. To address the above problems, a novel, fully deformable convolutional network (FD-Net) is proposed for dense and multiple-scale ship detection in HRSI, which could effectively extract features at variable scales, orientations and aspect ratios by integrating deformable convolution into the entire network structure. In order to boost more accurate spatial and semantic information flow in the network, an enhanced feature pyramid network (EFPN) is designed based on deformable convolution constructing bottom-up feature maps. Additionally, in considering of the feature level imbalance in feature fusion, an adaptive balanced feature integrated (ABFI) module is connected after EFPN to model the scale-sensitive dependence among feature maps and highlight the valuable features. To further enhance the generalization ability of FD-Net, extra data augmentation and training methods are jointly designed for model training. Extensive experiments are conducted on two public remote sensing datasets, DIOR and DOTA, which then strongly prove the effectiveness of our method in remote sensing field.

【 授权许可】

Unknown