RCSA-YOLO:改进YOLOv8的SAR舰船实例分割

RCSA-YOLO:Improved SAR Ship Instance Segmentation of YOLOv8

针对合成孔径雷达(synthetic aperture radar,SAR)图像中背景复杂、目标小和尺度变化大等导致分割精度低的问题,提出了一种基于改进YOLOv8的SAR图像舰船实例分割算法RCSA-YOLO。利用结构重参数技术设计特征提取模块RepBlock,用以替换原网络中的C2f模块,增强网络的特征提取和特征表达能力,有效过滤了复杂背景噪声的干扰。使用基于内容感知的特征重组模块(content-aware reassembly of features,CARAFE)替换最近邻上采样方法,有效缓解了小目标信息丢失现象,提升了分割精细化程度。使用可切换空洞卷积(switchable atrous convolution,SAC)进行下采样操作,动态调整感受野大小,使模型具备更强的多尺度适应能力,确保了在不同尺寸舰船目标上的分割精度。在HRSID数据集上的实验结果表明,提出的算法可以将YOLOv8模型的AP_(50)值从87.7%提高到90.7%,较原算法提高了3个百分点。与主流的实例分割算法对比,SAR舰船实例分割精度也明显提升,证明了RCSA-YOLO的有效性。

Addressing the challenges of low segmentation accuracy in synthetic aperture radar(SAR)images due to com-plex backgrounds,small targets,and large scale variations,the RCSA-YOLO algorithm is proposed.Firstly,the structural reparameterization technique is used to design the RepBlock feature extraction module,which replaces the original C2f module in the network.This improves the ability of network to extract and represent features,effectively filtering out interference from complex background noise.Secondly,the content-aware reassembly of features(CARAFE)module replaces the nearest neighbor upsampling method,reducing the loss of information in small targets and improving segmen-tation refinement.Finally,the switchable atrous convolution(SAC)is used for downsampling operations,allowing for the dynamic adjustment of receptive field size.This gives the model better adaptability to multiple scales and ensures segmen-tation accuracy for different sizes of vessels.Experimental evaluation on the HRSID dataset demonstrates that the pro-posed algorithm increases the AP_(50) value of the YOLOv8 model from 87.7%to 90.7%,surpassing the original algorithm by 3 percentage points.Comparative analysis against mainstream instance segmentation algorithms further reveals signifi-cant enhancement in SAR ship instance segmentation accuracy,validating the effectiveness of RCSA-YOLO.