A Hybrid Super-Resolution and Object Detection Framework for Small Ship Recognition in Optical Remote Sensing Imagery

Abstract

Continuous maritime surveillance increasingly relies on optical remote sensing, yet small ships are difficult to detect in low-resolution imagery because limited spatial resolution, complex sea backgrounds, and sensor noise suppress the fine-grained cues on which detectors depend, while routine acquisition of high-resolution imagery is prohibitively costly. To address this problem, a hybrid framework (RGT-YOLOv5Det) is proposed that couples transformer-based super-resolution (SR) with a lightweight object detector so that fine spatial detail is restored before detection. Methodologically, a paired benchmark (Ship-HRRSI/Ship-LRRSI) was constructed from the public TGRS-HRRSD dataset by standardising images to 800 × 800 pixels and generating 200 × 200 pixel counterparts via 4× bicubic down-sampling; three SR models (RGT, HAT, and Real-ESRGAN) and four detectors (YOLOv5s, YOLOv8s, YOLOv10s, and Faster R-CNN-MobileNetV3-Large-FPN) were fine-tuned and compared under identical training settings, and the best-performing components were integrated into the proposed two-stage pipeline. In the results, RGT delivered the best reconstruction quality (PSNR 22.038 dB, SSIM 0.3502) with the fewest parameters (13.37 M), YOLOv5s proved the most resolution-robust detector, and the integrated RGT-YOLOv5Det achieved mAP@0.5 of 0.947 and mAP@0.5:0.95 of 0.768 on low-resolution imagery, exceeding the best standalone detector score on each metric by 0.033 and 0.089, respectively. It is concluded that restoring structural detail prior to detection offers an accurate and acquisition cost-efficient alternative to high-resolution imaging, providing a practical route to reliable small ship detection in degraded optical remote sensing imagery.