Accelerated-YOLOv3 for Ship Detection from SAR Images

Abstract

Synthetic Aperture Radar (SAR) imagery has been widely used in many maritime applications due to its high resolution, wide coverage, and real-time monitoring characteristics. Nevertheless, the size of SAR images is significantly large for real-time application. In recent years, High-Performance Computing (HPC)-related methods have been used to improve the precision and detection rate of SAR imagery analysis. In this paper, motivated by the state-of-the-art real time object detection You Only Look Once version 3 (YOLOv3), an enhanced GPU-based deep learning method has been proposed, namely Accelereated-YOLOv3 (A-YOLOv3), to detect ships from the SAR images. A-YOLOv3 aims to reduce the computational time with relatively competitive detection accuracy by constructing a new architecture with less layers and channels. The proposed A-YOLOv3 architecture achieves Average Precision (AP) of 97.4% on the Expand Diversified SAR Ship Detection Dataset (EDSSDD).