ATSD: Anchor-Free Two-Stage Ship Detection Based on Feature Enhancement in SAR Images

Abstract

Syntheticap erture radar (SAR) ship detection in harbors is challenging due to the similar backscattering of ship targets to surrounding background interference. Prevalent two-stage ship detectors usually use an anchor-based region proposal network (RPN) to search for the possible regions of interest on the whole image. However, most pre-defined anchor boxes are redundantly and randomly tiled on the image, manifested as low-quality object proposals. To address these issues, this paper proposes a novel detection method combined with two feature enhancement modules to improve ship detection capability. First, we propose a flexible anchor-free detector (AFD) to generate fewer but higher-quality proposals around the object centers in a keypoint prediction manner, which completely avoids the complicated computation in RPN, such as calculating overlapping related to anchor boxes. Second, we leverage the proposed spatial insertion attention (SIA) module to enhance the feature discrimination between ship targets and background interference. It accordingly encourages the detector to pay attention to the localization accuracy of ship targets. Third, a novel weighted cascade feature fusion (WCFF) module is proposed to adaptively aggregate multi-scale semantic features and thus help the detector boost the detection performance of multi-scale ships in complex scenes. Finally, combining the newly-designed AFD and SIA/WCFF modules, we present a new detector, named anchor-free two-stage ship detector (ATSD), for SAR ship detection under complex background interference. Extensive experiments on two public datasets, i.e., SSDD and HRSID, verify that our ATSD delivers state-of-the-art detection performance over conventional detectors.