CLFR-Det: Cross-level feature refinement detector for tiny-ship detection in SAR images

Ship detection in synthetic aperture radar (SAR) images is an important and active topic, due to the characteristics of SAR images involving all-time and all-weather imaging. However, complex backgrounds caused by speckle noise and inshore land, coupled with information deficiency of tiny ships, pose a great challenge for tiny-ship detection in SAR images. To tackle this problem, we present a cross-level feature refinement detector (CLFR-Det) that utilizes features reflecting different levels and distinct semantics (classification and localization). To enrich the semantic information of tiny ships, our approach incorporates a cross-level modulated deformable convolution to aggregate features from relevant positions across multi-level feature maps. This is supported by a spatially-informed multi-scale feature refinement mechanism that combines the features for classification and those for localization. We implement a uniform IoU-weighted adaptive training sample selection method for equitably distributing the impact of positive samples from targets of various sizes during the training process. A generalized IoU loss between ground-truth and preliminary bounding box is further proposed to supervise the learning process of the CLFR-Det, with uncertainty weights incorporated to dynamically depict the levels of disparate losses, enabling adequate training across different tasks. Also, we construct a novel tiny SAR ship detection dataset to comprehensively evaluate the effectiveness of our system, in conjunction with the use of publicly available SSDD and HRSID datasets. Experimental investigations demonstrate that CLFR-Det generally surpasses state-of-the-art performance for multi-scale ship detection, particularly for the detection of tiny ships.