Balance Scene Learning Mechanism for Offshore and Inshore Ship Detection in SAR Images

ABOShips -- An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations

Availability of domain-specific datasets is an essential problem in object detection. Maritime vessel detection of inshore and offshore datasets is no exception, there is a limited number of studies addressing this need. For that reason, we collected a dataset of images of maritime vessels taking into account different factors: background variation, atmospheric conditions, illumination, visible proportion, occlusion and scale variation. Vessel instances (including 9 types of vessels), seamarks and miscellaneous floaters were precisely annotated: we employed a first round of labelling and subse…

具有无监督海陆分割的场景感知SAR船舶检测

Scene-aware SAR ship detection guided by unsupervised sea-land segmentation

本文提出了一种基于无监督海陆分割的场景感知SAR船舶检测方法，通过增强模型的海陆分割能力提升远海检测性能。

EcoSense：通过云边协作实现近岸船舶检测的节能智能感知

EcoSense: Energy-Efficient Intelligent Sensing for In-Shore Ship Detection through Edge-Cloud Collaboration

提出一种难度感知的云边协作感知系统，通过低功耗前端模型与基于transformer-图卷积网络的后端模型协同，在提升近岸船舶检测精度的同时大幅降低数据传输量与能耗。

NASTaR：NovaSAR自动船舶目标识别数据集

NASTaR: NovaSAR Automated Ship Target Recognition Dataset

本文提出NASTaR数据集，用于解决SAR图像中船舶类型分类的挑战，包含3415个船舶片段及AIS数据标注，验证了其在不同分类场景中的有效性，准确率超过87%。

Sparse R-CNN OBB：基于定向稀疏提议的SAR图像船舶目标检测

Sparse R-CNN OBB: Ship Target Detection in SAR Images Based on Oriented Sparse Proposals

本文提出Sparse R-CNN OBB框架，通过重新设计基线模型的检测头以捕捉目标方向，首次利用300个稀疏可学习提议实现SAR图像中的定向船舶目标检测，在近海和远海场景中均超越多数先进模型。

R-Sparse R-CNN：基于背景感知稀疏可学习提案的SAR船舶检测

R-Sparse R-CNN: SAR Ship Detection Based on Background-Aware Sparse Learnable Proposals

本文提出R-Sparse R-CNN框架，通过引入背景感知提案和双上下文池化及交互模块学习船舶与背景的上下文关系，在SAR图像定向船舶检测中显著提升了准确性与效率。