MBE-Net: A Multibranch Edge-Guided Feature Enhancement Algorithm for SAR Ship Detection

Abstract

Synthetic aperture radar (SAR) imagery plays a vital role in maritime surveillance, yet it faces severe challenges such as speckle noise, cluttered backgrounds, and the small scale and low contrast of ship targets. To overcome these issues, we propose MBE-Net, a lightweight and high-accuracy SAR ship detection network featuring multibranch edge-guided feature enhancement. First, the edge-enhanced wavelet backbone (EEWB) is designed to enhance edge information and suppress detail loss by integrating multibranch edge guidance and wavelet pooling, thereby improving small-ship perception in complex scenes. Second, the lightweight decoupled detail-enhanced head (LD-DEH) decouples classification and regression tasks while embedding a detail-enhanced convolution module to recover high-resolution features, significantly boosting localization precision under strong interference. Finally, the adaptive contextual focus feature pyramid network (ACF-FPN) introduces context-guided fusion to improve multiscale feature alignment and robustness, especially in low-contrast and cluttered environments. Extensive experiments on the HRSID and SSDD datasets validate that MBE-Net achieves excellent detection accuracy with minimal computational cost, making it highly suitable for real-time SAR ship detection.