LSAR-Det: A Lightweight YOLOv11-Based Model for Ship Detection in SAR Images

Due to its superior recognition accuracy, deep learning has been widely adopted in synthetic aperture radar (SAR) ship detection. Nevertheless, significant variations in ship target scales pose challenges for existing detection architectures, frequently leading to missed detections or false positives. Moreover, high-precision detection models are typically structurally complex and computationally intensive, resulting in substantial hardware resource consumption. In this letter, we introduce LSAR-Det, a novel SAR ship detection network designed to address these challenges. We propose a lightweight residual feature extraction (LRFE) module to construct the backbone network, enhancing feature extraction capabilities while reducing the number of parameters and floating-point operations per second (FLOPs). Furthermore, we design a lightweight cross-space convolution (LCSConv) module to replace the traditional convolution in the neck network. In addition, we incorporate a multiscale bidirectional feature pyramid network (M-BiFPN) to facilitate multiscale feature fusion with fewer parameters. Our proposed model contains merely 0.985M parameters and requires only 3.3G FLOPs. Experimental results on the SAR ship detection dataset (SSDD) and high-resolution SAR image dataset (HRSID) datasets demonstrate that LSAR-Det outperforms other models, achieving detection accuracies of 98.2% and 91.8%, respectively, thereby effectively balancing detection performance and model efficiency.