AR2Det: An Accurate and Real-Time Rotational One-Stage Ship Detector in Remote Sensing Images

Ship detection plays a significant role in the high-resolution remote sensing (HRRS) community, but it is a challenging task due to the complex contents within HRRS images and the diverse orientation of ships. Recently, with the development of deep learning, the performance of the HRRS ship detection model has been improved greatly. Most of them employ deep networks and complicate anchor mechanism to get well ship detection results. Nevertheless, this kind of combination limits the detection efficiency. To address this problem, a new approach named accurate and real-time rotational ship detector (AR(2)Det) is proposed in this article to detect ships without the anchor mechanism. Based on the extracted features by the feature extraction module (FEM) and the central information of ships, AR(2)Det adopts two simple modules, ship detector (SDet) and center detector (CDet), to generate and improve the detection results, respectively. AR(2)Det is efficient due to the simple postprocessing and the lightweight network. Also, AR(2)Det performs satisfactorily due to the effective generation and enhancement strategy of bounding boxes. The extensive experiments are conducted on a public HRRS image ship detection dataset HRSC2016. The promising results show that our method outperforms the state-of-the-art approaches in terms of both accuracy and speed.

Automated summary

Ship detection in HRRS images is challenging due to complex contents and diverse orientation, but with the development of deep learning, the performance has greatly improved. The AR(2)Det method proposed in this article achieves efficient ship detection without the anchor mechanism, outperforming state-of-the-art approaches in terms of accuracy and speed.