Dense-Weak Ship Detection Based on Foreground-Guided Background Generation Network in SAR Images

Wenping Ma, Xiaoting Yang, Hao Zhu, Xiaoteng Wang, Biao Hou, Mengru Ma, Yue Wu

Abstract: Currently, ship detection based on synthetic aperture radar (SAR) images still faces significant challenges, particularly in detecting weak and densely distributed ships within complex backgrounds. In areas such as ports and land, the complex background features often resemble those of densely distributed ships, leading to reduced detection accuracy. In addition, the overlapping and mutual interference of features among dense ships can cause the network to miss detections or produce false positives. Therefore, this article proposes a foreground-guided background generation network (FGBG-Net), which includes a Gaussian foreground localization (GFL) model and a background feature removal (BFR) module. The GFL module identifies the approximate high-probability regions of ship foregrounds on the feature map, guiding the network to focus on these regions. The BFR module then progressively removes background interference features based on the positions provided by the GFL module, generating feature maps that are more suitable for detecting weak and dense ships. Our network has been validated on multiple SAR ship datasets, and the experimental results demonstrate noticeable performance improvements, with a mean average precision (mAP) increase of 3.4% on the SSDD and HRSID datasets. The relevant code is available at the following link: https://github.com/Xidian-AIGroup190726/FBGBNet/tree/master

External IDs:dblp:journals/tgrs/MaYZWHMW25