ELLK-Net: An Efficient Lightweight Large Kernel Network for SAR Ship Detection

ELLK-Net, an efficient, lightweight network with a large kernel, is proposed for synthetic aperture radar (SAR) ship detection. It addresses background variations, different ship scales, and noise interference challenges. ELLK-Net uses an anchor-free detector framework and sequentially decomposes large kernel convolutions to capture comprehensive global information and long-range dependencies. It adaptively selects convolution kernels on the basis of target characteristics, enhancing multiscale feature expression. A novel large kernel multiscale attention (LKMA) module is introduced to enhance interlayer feature fusion and semantic alignment, mitigating the impacts of overlapping ships and scattering noise. Structural reparameterization techniques optimize inference speed across devices without compromising accuracy. The experimental results on the SAR ship detection dataset (SSDD) and high-resolution SAR image dataset (HRSID) datasets demonstrate that ELLK-Net achieves impressive AP50 values of 95.6% and 90.6% for horizontal box detection and 89.7% and 79.7% for rotating box detection, respectively. The reparameterized detector exhibits a significant 48.7% FPS improvement on the Nvidia Jetson NX platform, indicating its suitability for edge computing deployment. The code is available at https://github.com/CHD-IPAC/ELLK-Net.