Improved region convolutional neural network for ship detection in multiresolution synthetic aperture radar images
Qilin Xiao, Yun Cheng, Minlei Xiao, Jun Zhang, Hongji Shi, Lihui Niu, Chenguang Ge, Haitao Lang
Abstract
Summary Effectively obtaining the location and direction of the ship target is an important prerequisite for maritime traffic management and marine accident rescue. Thanks to the rapid development of the target detection methods based on deep learning, this article proposed a ship target detection method for multiresolution synthetic aperture radar (SAR) images based on improved region convolution neural network (R‐CNN). According to the characteristics of ship target in the SAR images, we make several improvements such as enlarging the input, proposal optimization, database target categorization, and weight balance on the basis of the standard Faster R‐CNN. The experimental results proved that the proposed method can detect target effectively and precisely in complicated scenes of multiresolution SAR images, such as in‐shore and dense targets. It has a good potential in practical application.