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Proceedings Paper

Remote sensing image ship detection based on feature pyramid
Author(s): Lamei Zou; Changfeng Li; Weidong Yang; Shiyang Zhou; Shiwei Nie
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Paper Abstract

We present a two-stage method for remote sensing image ship detection. The proposed approach efficiently detects ships in remote sensing images. Firstly, a light-weight classification network is used to classify different regions. In second stage, we design a detection framework to detect ships in sub-images, which are considered to contain object in the first stage. To solve the scale problems in object detection, our detection network is built on feature pyramid network, but we explicitly assign object into corresponding feature maps based on size. In our proposed framework, instead of using anchors, we predict object center point and the offsets to bounding box. The experiment results show that our proposed method has a good performance in terms of speed and accuracy.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 1143013 (14 February 2020); doi: 10.1117/12.2539136
Show Author Affiliations
Lamei Zou, Huazhong Univ. of Science and Technology (China)
Changfeng Li, Huazhong Univ. of Science and Technology (China)
Weidong Yang, Huazhong Univ. of Science and Technology (China)
Shiyang Zhou, Huazhong Univ. of Science and Technology (China)
Shiwei Nie, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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