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Ship Segmentation and Orientation Estimation Using Keypoints Detection and Voting Mechanism in Remote Sensing Images

  • Mingxian Nie
  • Jinjie Zhang
  • Xuetao ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)

Abstract

Ship detection in remote sensing images is an important and challenging task in civil fields. However, the various types of ships with different scale and ratio and the complex scenarios are the main bottlenecks for ship detection and orientation estimation of the ship. In this paper, we propose a new method based on Mask R-CNN, which can perform ship segmentation and direction estimation on ships at the same time by simultaneously output the binary mask and the bow and sterns keypoints locations. We can achieve keypoints detection of the ship without significantly losing the accuracy of the mask. Finally, we regress the coordinates of the ship’s bow and sterns to four quadrants and use the voting mechanism to determine which quadrant the bow keypoint locates. Then we combine the quadrant of bow keypoint with the minimum bounding box of the mask to determine the final orientation of the ship. Experiments on the datasets have achieved effective performance.

Keywords

Remote sensing images Ship detection Orientation estimation Mask R-CNN 

Notes

Acknowledgments

This work was supported by the National Science and Technology Major Project of China grant number 2018ZX01008103 and National Key Research and Development Program of China under Grant 2017YFC0803905.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Software EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina

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