Sea Surface Object Detection Based on Background Dynamic Perception and Cross-Layer Semantic Interaction

Sea surface object detection plays an important role in the coastal defense monitoring system. Existing target detection methods mostly lack the adaptive perception of background changes. In addition, these methods fail to further integrate and interact with the multi-layer features extracted from the deep backbone network. To address these two issues, we first propose a Background Dynamic Perception module, which uses environmental information as an auxiliary. We train the detector to dynamically capture the background changes through a multi-task learning framework. Moreover, we propose a Cross-Layer Semantic Interaction module, which can achieve cross-layer interaction and reduce information loss. Based on the above modules, we propose a sea surface object detection network. To verify the performance, we collected real sea surface data and built a sea surface object dataset. Experimental results demonstrate that our method achieves 74.4% AP on the dataset, outperforming the latest methods.