Scale-Mixing Enhancement and Dual Consistency Guidance for End-to-End Semisupervised Ship Detection in SAR Images

The object detection of synthetic aperture radar (SAR) ships holds significant promise for water traffic monitoring, ship search and rescue, and maritime warning tasks. Regrettably, most of the existing SAR ship object detection is limited to the fully supervised paradigm, exhibiting a firm reliance on data labels, and inherent challenges such as multiscale ship feature disparities and indistinct small-sized ships also make SAR ship detection difficult. To address these, we propose a scale-mixing enhanced and dual consistency guided semisupervised object detection (SMDC-SSOD) method. Specifically, this method is based on the teacher–student framework and primarily comprises three core components: cross-scale feature mixing (CSFM) scheme, scale change consistency guidance (SCCG) strategy, and proposal consistency guidance (PCG) strategy, which can efficiently conduct end-to-end semisupervised learning from limited data labeling, achieving low-cost and high-performance ship perception. CSFM scheme includes interpyramid and intrapyramid feature cross-scale mixings, which can improve the network's adaptability for multiscale ship characteristics and increase focus on small-sized ships. SCCG strategy leverages variations in confidence scores at different scales to select valuable pseudolabels, providing more precise guidance for the student network. PCG strategy further reflects the positioning quality of pseudolabels through the proposal consistency generated by the student network, guiding it to make high-quality predictions. The experimental results on the publicly available HRSID, BBox-SSDD, and SAR-Ship-Dataset demonstrate that SMDC-SSOD can accurately detect SAR ships with an extremely low data annotation rate (below 10%) and achieve optimal detection performance compared to state-of-the-art methods.