Asymmetric Aggregation Network for Accurate Ship Detection in Optical Imagery

Optical imagery ship detection has achieved significant developments recently. However, accurate detection in complex scenes and for different-scale ships remains a vital challenge. To solve the above issues, in this article, we propose the asymmetric aggregation feature pyramid network (A2FPN), incorporating top-down semantic aggregation and bottom-up detail enhancement to propagate semantic and detailed information across different feature levels. In particular, the higher-level hierarchical features propagate global semantic information to the lower-level hierarchical features, successively enhancing the discriminative ability of each level of hierarchical features. After that, the lower-level hierarchical features with abundant semantic information are also aggregated successively to the higher-level hierarchical features through the augmentation path, enriching the details of each level of hierarchical features. Considering the real-time requirements of ship detection, we replace the original path aggregation feature pyramid network (FPN) of YOLOX with the proposed A2FPN and develop a ship detection model termed asymmetric aggregation network (A2Net). Extensive experiments are performed on the three commonly used ship detection datasets, ShipRSImageNet, Seaships7000, and HRSC2016. Quantitative and qualitative results demonstrate that A2Net outperforms the state-of-the-art methods.