Sea Surface Floating Small-Target Detection Based on Dual-Feature Images and Improved MobileViT

Small-target detection in sea clutter is a key challenge in marine radar surveillance, crucial for maritime safety and target identification. This study addresses the challenge of weak feature representation in one-dimensional (1D) sea clutter time-series analysis and suboptimal detection performance for sea surface small targets. A novel dual-feature image detection method incorporating an improved mobile vision transformer (MobileViT) network is proposed to overcome these limitations. The method converts 1D sea clutter signals into two-dimensional (2D) fused images by means of a Gramian angular difference field (GADF) and recurrence plot (RP), enhancing the model’s key-information extraction. The improved MobileViT architecture enhances detection capabilities through multi-scale feature fusion with local–global information interaction, integration of coordinate attention (CA) for directional spatial feature enhancement, and replacement of ReLU6 with SiLU activation in MobileNetV2 (MV2) modules to boost nonlinear representation. Experimental results on the IPIX dataset demonstrate that dual-feature images outperform single-feature images in detection under a 10−3 constant false-alarm rate (FAR) condition. The improved MobileViT attains 98.6% detection accuracy across all polarization modes, significantly surpassing other advanced methods. This study provides a new paradigm for time-series radar signal analysis through image-based deep learning fusion.