A Direction-Aware Inshore Ship Detection Method for SAR Images

In areas such as maritime rescue, fisheries management, traffic surveillance, and national defense, ship detection and recognition based on synthetic aperture radar pictures is crucial. Deep learning offers a fresh approach to high-performance detection and recognition for SAR ships in the past few years with the growth of artificial intelligence. This work examines the ship target recognition approach in SAR images and suggests a direction-aware inshore ship detection method in order to meet the challenges of multi-scale ship target detection in SAR images as well as the complicated background of ships stationed in ports. Multiscale features are observed by using the pyramid feature extraction module with attention method. Aiming at the phase ambiguity problem in OBB regression, the direction-aware classification regression head was designed to accurately determines the position and direction of ship targets. Finally, the experimental part verifies that the proposed method reduces the computational complexity of our method and ensuring the detection performance.