Enhancing SAR Aircraft Detection with CCS-Net: A Lightweight and Efficient Framework for Manned–Unmanned Teaming Reconnaissance

In contemporary manned–unmanned teaming (MUM-T) systems, the accurate detection of aircraft in Synthetic Aperture Radar (SAR) imagery is crucial for battlefield surveillance and target identification. However, challenges such as background clutter, significant scale variations, and limitations in existing feature extraction methods hinder detection accuracy. To address these issues, this study proposes CCS-Net, a lightweight Cooperative Context-aware Sensing Network designed specifically for SAR aircraft detection. CCS-Net enhances image contrast through Contrast-Limited Adaptive Histogram Equalization (CLAHE) preprocessing and employs a novel C2F_LK module combined with a Multi-scale Context Aggregation (MSCA) module to improve multi-scale feature representation with minimal parameters. An FPN-PAN structure adaptively fuses these features, while the Spatial Coordinate Attention Head (SCA-Head) integrates spatial and coordinate attention mechanisms to emphasize key aircraft regions. Optimized with label smoothing, our model achieves a 96.6% [email protected] and 93.5% precision on the SAR-Aircraft-1.0 dataset with only 0.97M parameters. It generalizes well on the SADD aircraft dataset and cross-domain HRSID and SSDD ship benchmarks, outperforming state-of-the-art methods in both accuracy and lightweight design.