Shape-Prior Dynamic Refinement Network with Diffusion Wavelets for Fine-Grained Ship Detection in Remote Sensing Images

Fine-grained ship detection in remote sensing images is important for maritime surveillance and port management. Although deep learning methods have achieved promising results, fine-grained ship detection remains difficult because of complex background interference and difficulty in distinguishing similar objects. To address these limitations, we propose a Shape-Prior Dynamic Refinement Network (SPDR-Net) for fine-grained ship detection. First, we design a Gaussian Mixture Shape Representation (GMSR) mechanism to guide the learning of shapes and texture details by learnable Gaussian mixture models. Second, we develop Re-Parameterized Selective Dynamic Kernels (RSDKs) based on the GMSR shape prior to dynamically adjust the receptive field of each scale to more accurately capture morphological features. Furthermore, a Diffusion-Supervised Wavelet Refinement (DSWR) strategy is developed, which brings diffusion-based noising–denoising adversarial learning mechanism into wavelet reconstruction to recover high-frequency details, strengthening boundaries and fine-grained differential information while suppressing noise. Extensive experiments on public datasets demonstrate that the proposed method achieves state-of-the-art performance with mAP50s of 79.3% on DOSR, 47.97% on FAIR1M, 98.1% on HRSC2016, and 97.7% on SSDD, respectively. It exhibits that SPDR-Net effectively handles both offshore and complex nearshore scenarios, offering a robust solution for fine-grained ship detection.