@inproceedings{3d4300091adf495293cacfb54467ea86,
title = "Ship Segmentation and Orientation Estimation Using Keypoints Detection and Voting Mechanism in Remote Sensing Images",
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{\textquoteright}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 = "Mask R-CNN, Orientation estimation, Remote sensing images, Ship detection",
author = "Mingxian Nie and Jinjie Zhang and Xuetao Zhang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 16th International Symposium on Neural Networks, ISNN 2019 ; Conference date: 10-07-2019 Through 12-07-2019",
year = "2019",
doi = "10.1007/978-3-030-22808-8\_39",
language = "英语",
isbn = "9783030228071",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "402--413",
editor = "Huchuan Lu and Huajin Tang and Zhanshan Wang",
booktitle = "Advances in Neural Networks {\textendash} ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings",
}