MidNet: An Anchor-and-Angle-Free Detector for Oriented Ship Detection in Aerial Images

Ship detection in aerial images remains an active yet challenging task due to its arbitrary object orientation and various aspect ratios from the bird's-eye perspective. Most existing oriented objection detection methods rely on angular prediction or predefined anchor boxes, making these method...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2023-01, Vol.61, p.1-1
Main Authors: Liang, Yuping, Feng, Jie, Zhang, Xiangrong, Zhang, Junpeng, Jiao, Licheng
Format: Article
Language:English
Subjects:
aerial images
Algorithms
anchor-and-angle-free detector
Aspect ratio
Boxes
Coding
Convolution
Deep learning
Detection
Detectors
Encoding
Feature extraction
Formability
Geometry
Marine vehicles
Object detection
ship detection
Ships
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Summary:Ship detection in aerial images remains an active yet challenging task due to its arbitrary object orientation and various aspect ratios from the bird's-eye perspective. Most existing oriented objection detection methods rely on angular prediction or predefined anchor boxes, making these methods highly sensitive to unstable angular regression and excessive hyper-parameter setting. To address these issues, we replace the angular-based object encoding with an anchor-and-angle-free paradigm, and propose a novel detector deploying a center and four midpoints for encoding each oriented object, namely MidNet. Moreover, MidNet designs a novel symmetrical deformable convolution for enhanceing the features of midpoints, then the center and midpoints for an identical ship are adaptively matched by predicting corresponding centripetal shift and matching radius. Finally, a concise analytical geometry algorithm is proposed to calculate the ship orientation and refine the keypoints step-wisely for building precise oriented bounding boxes. On two public ship detection datasets, HRSC2016 and FGSD2021, MidNet outperforms the state-of-the-art detectors by achieving APs of 90.52% and 86.50%.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3280973