A Lightweight SAR Ship Detector Using End-to-End Image Preprocessing Network and Channel Feature Guided Spatial Pyramid Pooling

Recently, in the field of synthetic aperture radar (SAR) ship detection, deep-learning-based methods have made significant strides in terms of detection accuracy and speed. However, small-scale targets and complex backgrounds remain a formidable obstacle to SAR ship detection. To overcome the aforementioned challenges, this letter proposes LiteSAR-Net, a lightweight SAR ship detector, to enhance ship detection capabilities in SAR imagery. In detail, an end-to-end image preprocessing network (E2IPNet) is proposed to strengthen context information and expand the network's effective receptive field. In addition, to prevent the dilution of semantic information, the channel feature guided spatial pyramid pooling (CFGSPP) is proposed, which can adjust the parameters adaptively based on interchannel information. The proposed LiteSAR-Net achieved an average precision (AP) of 98.61% on the SAR ship detection dataset (SSDD) and 93.33% on the high-resolution SAR images dataset (HRSID), with a parameter of only 5.247 M, outperformed many state-of-the-art (SOTA) detectors.