Experimental details table.

Ship object detection and fine-grained recognition of remote sensing images are hot topics in remote sensing image processing, with applications in fishing vessel operation command, merchant ship navigation route planning, and other fields. In order to improve the detection accuracy for different types of remote sensing ship objects, this paper proposes a ship object perception and feature refinement method based on the improved ReDet, called Mamba-ReDet (M-ReDet). First, this paper designs a ship object fine-grained feature extraction backbone (Mamba-ReResNet, M-ReResNet), which selects and reconstructs the unique features of different types of ship objects through the Mamba’s selective memory to improve the algorithm’s ability to extract fine-grained features. Secondly, the M-ReDet consists of the Ship Object Perception Module (SOPM) and the Ship Feature Refinement Module (SFRM), which can extract the ship’s spatial position information from the feature map, fuse different scales of spatial position information and use this information to refine the fine-grained features to improve the detection accuracy of the algorithm for different categories of ships. Finally, we use the KFIoU and Focal Loss as the regression loss and classification loss of the algorithm to improve the accuracy of the training. The experimental results show that the mAP0.5 of the M-ReDet algorithm on the FAIR1M(ship) and DOTAv1.0 visible light (RGB) remote sensing image datasets are 43.29% and 82.09%, respectively, which is 2.78% and 3.34% higher than that of the ReDet.

遥感图像船舶目标检测与细粒度识别是遥感图像处理领域的热点研究课题，其应用场景覆盖渔船作业指挥、商船航线规划等多个领域。为提升不同类型遥感船舶目标的检测精度，本文提出一种基于改进ReDet的船舶目标感知与特征细化方法，命名为Mamba-ReDet（简称M-ReDet）。首先，本文设计了船舶目标细粒度特征提取主干网络（Mamba-ReResNet，简称M-ReResNet），该网络通过Mamba的选择性记忆机制筛选并重构不同类型船舶目标的独有特征，以提升算法的细粒度特征提取能力。其次，M-ReDet由船舶目标感知模块（Ship Object Perception Module, SOPM）与船舶特征细化模块（Ship Feature Refinement Module, SFRM）组成，可从特征图中提取船舶的空间位置信息，融合多尺度空间位置信息，并利用该信息对细粒度特征进行细化，进而提升算法对不同类别船舶的检测精度。最后，本文采用KFIoU与Focal Loss分别作为算法的回归损失与分类损失，以提升训练精度。实验结果表明，M-ReDet算法在FAIR1M（船舶子集）与DOTAv1.0可见光（RGB）遥感图像数据集上的mAP@0.5分别为43.29%与82.09%，较原始ReDet算法分别提升2.78%与3.34%。