NRENet: Neighborhood removal-and-emphasis network for ship detection in SAR Images
Wenping Ma, Xiaoting Yang, Hao Zhu, Xiaoteng Wang, Xiaoyu Yi, Yue Wu, Biao Hou, Licheng Jiao
Abstract
In recent years, object detection in Synthetic-Aperture Radar (SAR) images still faces many challenges, especially for ship detection. Small or dense ships are vulnerable to the interference of complex scenes such as ports and land. In feature extraction, a large amount of redundant information on the feature map will further reduce the network’s attention to small-sized ships. Therefore, in this paper, we propose a network called neighborhood removal-and-emphasis network (NRE-Net), including an object neighborhood removal (ONR) strategy and a neighborhood feature emphasis (NFE) module. Among them, the ONR strategy directly removes the complex background information around the ship before feature extraction, only retaining effective contextual neighborhoods conducive to ship detection and avoiding the interference of complex background information on the network. The NFE module is based on ONR to extract features and form a weight map of small-sized ships or complex images. This module can adaptively recognize the detection neighborhood of each ship and highlight the detection ship on the feature map. Our network has validated the effectiveness of the method on multiple SAR ship datasets and improved the object AP for each size. Our code is available at: https://github.com/Xidian-AIGroup190726/NRENet . • Proposed strategy mitigates invalid neighborhoods in SAR for ship detection. • Particle Swarm Optimization optimizes network for superior detection neighborhoods. • Use the obtained best neighborhood to emphasize the features of ship.