Arbitrary-Oriented Ship Detection through Center-Head Point Extraction

TL;DR

This paper introduces CHPDet, a novel detector for arbitrary-oriented ship detection in remote sensing images, utilizing center-head point extraction and orientation-invariant features to improve accuracy and reduce hyper-parameters.

Contribution

The paper proposes a new center-head point extraction method for arbitrary-oriented ship detection, addressing angle prediction inaccuracies and hyper-parameter issues in existing methods.

Findings

Achieves state-of-the-art performance on FGSD2021, HRSC2016, and UCAS-AOD datasets.

Effectively distinguishes between bow and stern of ships.

Reduces computational cost compared to anchor-based methods.

Abstract

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 hyper-parameters 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 paper, we propose a center-head point extraction based detector (named CHPDet) to achieve arbitrary-oriented ship detection in remote sensing images. Our CHPDet formulates arbitrary-oriented ships as rotated boxes with head points which are used to determine the direction. And 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 finetune the results. Moreover, we introduce a new dataset for multi-class arbitrary-oriented ship detection in remote sensing images at a fixed ground sample distance (GSD) which is named FGSD2021. Experimental results on FGSD2021 and two other widely used data sets, i.e., HRSC2016, and UCAS-AOD demonstrate that our CHPDet achieves 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.