A regional attention-based detector for SAR ship detection

Automatic ship detection in Synthetic Aperture Radar (SAR)imagery has been playing a significant role in the field of marine monitoring. But great challenges still exist in real-time application. Despite the exciting progress made by deep-learning techniques, most detectors failed to yield locations of fairly high quality, especially for small objects under the complicated background. To alleviate the above problem, the author proposes a single-stage detector based on the attention mechanism. First, we degenerate pixel-level semantic segmentation into box-level segmentation to suppress background interference. The attention map generated from weak segmentation roughly locates the region of interest through automatic learning. Besides, it has a top-down feature pyramid structure embedded with the multi-branch fusion module. With more detailed features and richer semantic information, it can detect multi-scale and multi-directional targets more effectively. Experiments on the SAR ship dataset have achieved a promising result.