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Arbitrary-Oriented Ship Detection Through Center-Head Point Extraction
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AI
DOI：10.1109/TGRS.2021.3120411.png)
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摘要
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Ship detection in remote sensing images plays a crucial role in various applications and has drawn increasing attention in recent years. However, existing arbitrary-oriented ship detection methods are generally developed on a set of predefined rotated anchor boxes. These predefined boxes not only lead to inaccurate angle predictions but also introduce extra hyperparameters and high computational cost. Moreover, the prior knowledge of ship size has not been fully exploited by existing methods, which hinders the improvement of their detection accuracy. Aiming at solving the above issues, in this article, we propose a center-head point extraction-based detector (CHPDet) to achieve arbitrary-oriented ship detection in remote sensing images. Our CHPDet formulates arbitrary-oriented ships as rotated boxes with head points that are used to determine the direction. Also, a rotated Gaussian kernel is used to map the annotations into target heatmaps. Keypoint estimation is performed to find the center of ships. Then, the size and head point of the ships are regressed. The orientation-invariant model (OIM) is also used to produce orientation-invariant feature maps. Finally, we use the target size as prior to fine-tune the results. Moreover, we introduce a new dataset for multiclass arbitrary-oriented ship detection in remote sensing images at a fixed ground sample distance (GSD) that is named FGSD2021. Experimental results on FGSD2021 and two other widely used datasets, i.e., HRSC2016 and UCAS-AOD, demonstrate that our CHPDet achieves the state-of-the-art performance and can well distinguish between bow and stern. Code and FGSD2021 dataset are available at https://github.com/zf020114/CHPDet.
Keyword:
Marine vehicles
Detectors
Head
Remote sensing
Feature extraction
Object detection
Task analysis
Arbitrary-oriented ship detection
deep convolution neural networks
keypoint estimation
remote sensing images
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
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