Arbitrary-Oriented Ship Detection Method Based on Improved Regression Model for Target Direction Detection Network

Arbitrary-oriented ship detection is one of the main applications of high-resolution remote sensing images. The current target direction detection methods, based on deep convolution neural network (DCNN), can estimate most of the ship directions (i.e. represented by angles). However, the performance of these networks is limited by the boundary discontinuity problem due to the angle-based regression model. In this paper, we propose an improved regression model based on coordinates in the complex plane to solve the boundary discontinuity problem. Experiments on real remote sensing dataset verify the effectiveness and robustness of our method.