Small Ship Detection via Deformable Convolutional Network
Yao Wang, Ganggang Dong, Hongwei Liu · 2021
Though widely studied, target detection in synthetic aperture radar (SAR) image is still a challenging problem. The classical convolutional neural network (CNN) samples spatial locations with the fixed geometric structure, and hence is incapable of learning the representations of varied-scale ships. It's difficult to locate multi-scale targets accurately in the complex scenes. On the other hand, to apply the classical models in SAR image, we need to duplicate the single-channel image to 3-channel one. The preprocess could not introduce the additional semantic information yet producing feature redundancy. To solve these problems, we introduced a new method for ship detection. We deployed the deformable convolutional block to learn features of ships with various scales at arbitrary locations. Different from the preceding works, the former shallow feature maps are also employed to enhance the representations of targets, especially small ships. In addition, the group normalization strategy is configured to alleviate internal covariate shift and accelerate the convergence. There is no need to make a tradeoff between the scale of model architecture and the batch size. Multiple comparative experiments on SSDD dataset are performed to demonstrate the advantage of proposed methods.