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.
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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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Nie, M., Zhang, J., Zhang, X. (2019). Ship Segmentation and Orientation Estimation Using Keypoints Detection and Voting Mechanism in Remote Sensing Images. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_39
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DOI: https://doi.org/10.1007/978-3-030-22808-8_39
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