Ship Detection Based on RetinaNet-Plus for High-Resolution SAR Imagery

Abstract

Ship detection in high-resolution synthetic aperture radar (SAR) imagery is a fundamental and challenging problem due to the complex environments. In this paper, a RetinaNet-Plus method is presented for ship detection in high-resolution SAR imagery based on RetinaNet network modified. In this approach, instead of setting the score for neighboring region proposals to zero as in Non-Maximum Suppression (NMS), Soft-NMS decreases the detection scores as an increasing function of overlap to avoid loss of precision. In addition, focal loss is used to address the class imbalance and to increase the importance of the hard examples during training. The experiments on SAR ship SSDD dataset and TerraSAR-X image from Barcelona port, show that our method is more accurate than the existing algorithms and is effective for ship detection of high-resolution SAR imagery.