SMEP-DETR: Transformer-Based Ship Detection for SAR Imagery with Multi-Edge Enhancement and Parallel Dilated Convolutions
Chushi Yu, Yoan Shin
Abstract
Synthetic aperture radar (SAR) serves as a pivotal remote sensing technology, offering critical support for ship monitoring, environmental observation, and national defense. Although optical detection methods have achieved good performance, SAR imagery still faces challenges, including speckle, complex backgrounds, and small, dense targets. Reducing false alarms and missed detections while improving detection performance remains a key objective in the field. To address these issues, we propose SMEP-DETR, a transformer-based model with multi-edge enhancement and parallel dilated convolutions. This model integrates a speckle denoising module, a multi-edge information enhancement module, and a parallel dilated convolution and attention pyramid network. Experimental results demonstrate that SMEP-DETR achieves the high mAP 98.6% on SSDD, 93.2% in HRSID, and 80.0% in LS-SSDD-v1.0, surpassing several state-of-the-art algorithms. Visualization results validate the model’s capability to effectively mitigate the impact of speckle noise while preserving valuable information in both inshore and offshore scenarios.