Synthetic Aperture Radar (SAR) ship target detection is crucial for marine monitoring, offering vital support for maritime security, navigation safety, and environmental surveillance. However, deploying advanced deep learning models on resource-constrained edge devices like UAVs and spaceborne platforms is challenging due to the high computational complexity and large parameter counts, hindering real-time performance. To address this, we propose the HGXES model, a lightweight SAR ship detection network. This model integrates efficient structural design, feature enhancement mechanisms, and an attention mechanism to reduce computational costs while preserving feature extraction capabilities. It employs factorized convolutions, a cross-level feature reuse module, and an attention mechanism to dynamically adjust feature weights, enhancing sensitivity to ship targets. A lightweight detection head ensures rapid and accurate target classification and localization. Experiments on benchmark SAR datasets show that based on the lightweight HGNetV2 backbone, our incremental designs (Xfeat, ELA, LWDetect) further compress the model and achieve a 70% reduction in parameters compared with traditional models, with a model size of just 1.9 MB, 2.3 M parameters, and 3.9 G FLOPs, achieving 49.7 fps detection speed. Comparative analyses reveal the superiority of the ELA attention mechanism and ShapeIoU loss function in enhancing performance. Thus, the HGXES model successfully achieves lightweight SAR ship detection, supporting real-time marine monitoring on resource-limited platforms with high accuracy and reduced computational costs.
Tian et al. (Thu,) studied this question.