MSCF-Net: A Lightweight Multiscale SAR Image Oriented Ship Detection Algorithm in Complex Scenes
Zihua Chen, Peng Zhang, Peng Liu, Mengwei Li · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2025
To address the challenges in synthetic aperture radar (SAR) ship detection, including complex target back-grounds, multi-scale ships, and diverse orientations, while also meeting the lightweight requirements for satellite-based appli-cations, we propose a lightweight multi-scale feature fusion network—MSCF-Net. First, we design a lightweight DRC2F module that enhances the network's multi-scale feature extr-action capability through a two-stage residual mechanism and multi-scale depth-wise separable dilated convolutions. Second, to overcome the limitations of traditional feature fusion methods in SAR ship detection, we propose a multi-scale channel feature fusion pyramid network, MSCF-FPN, which effectively sup-presses background noise interference while highlighting foreground target features through multi-modal pooling and dynamic feature calibration mechanisms. Finally, to further improve detection accuracy on SAR images characterized by low target-background discriminability and diverse target orient-ations, we propose a multi-branch decoupled detection head integrated with receptive-field attention to improve the detection head's capacity to perceive spatial as well as orientation information. Experimental results demonstrate that MSCF-Net achieves detection accuracies of 79.1% (+4.9%) and 92.1% (+2%) on the SRSDD-v1.0 and HRSID, respectively, with mean average precisions (mAP) of 71.7% (+5.8%) and 93.3% (+0.2%). Further-more, the number of model parameters is decreased from 10.89M to 4.66M, a reduction of approximately 57.2%, striking an effective balance between detection performance and model efficiency. In addition, MSCF-Net exhibits robust generalization capabilities on large-scene SAR images, rendering it well-suited for application in complicated real-world scenarios.