A High-Effective Implementation of Ship Detector for SAR Images

Synthetic aperture radar (SAR) can be applied to observe the sea surface and detect ship targets. Images obtained by SAR can be hard to read because of the denseness of ships, extremely unbalanced foreground-background, and small target size. The existing objective detection approaches achieve superior performance by sacrificing detection speed and flexibility on computational resource due to redundant model parameters. These features happens to be extremely crucial for SAR ship detection in real-time scenarios. To effectively tradeoff the issue on both time and space complexity, we propose a novel objective detection method tailored for SAR ship detection problem. We examine the effectiveness of our method on two open datasets: high resolution SAR images dataset (HRSID) and large-scale SAR ship detection dataset-v1.0 (LS-SSDD-V1.0). The results show better performance over the state-of-the-art You Only Look Once version 4 (YOLOv4) framework in terms of accuracy, efficiency, and model complexity.

Automated summary

This study introduces a novel objective detection method tailored for SAR ship detection, which has been proven to outperform the state-of-the-art YOLOv4 framework in terms of accuracy, efficiency, and model complexity through experiments on open datasets.