基于改进YOLOv8s的SAR图像舰船目标检测方法
Ship Target Detection Method in SAR Images Based on Improved YOLOv8s
杨明秋 1左小清 1董燕1
作者信息
- 1. 昆明理工大学国土资源工程学院,云南 昆明 650093
- 折叠
摘要
针对现有算法在合成孔径雷达(SAR)图像舰船目标检测任务中存在检测精度不高、易漏检、模型体量大不易部署、实时性不高等问题,提出了一种基于YOLOv8s改进的轻量SAR图像舰船目标检测模型.首先,提出了一种轻量残差特征增强模块ACC,该模块采用自适应池化的方式来提取不同的上下文信息,用残差增强的方式减少丢失特征金字塔中处于最高层次的特征信息,改善高层次特征的表达,提升网络对小目标的检测能力;然后,引入轻量动态蛇形卷积(DSConv),替换标准卷积操作的功能,优化模型对细小条形舰船目标的检测效果,减少目标漏检;最后,融合轻量BiFormer动态稀疏注意力模块,进一步优化小目标检测效果.在公开的SAR 影像舰船目标检测数据集(SSDD)上,所提出的算法比原算法在精确度(Precision)、召回率(Recall)、平均精度均值(mAP@0.5)上分别提升了5.0、3.6、3.3百分点,显著提高了模型对SAR图像舰船目标的检测能力.在高分辨率SAR图像数据集(HRSID)上进行模型的泛化实验结果表明,所提模型的图像检测效果优于其他经典模型.
Abstract
In response to the problems of low detection accuracy,missed detections,large model size unsuitable for deployment,and low real-time performance of existing algorithms in synthetic aperture radar(SAR)image ship target detection tasks,an improved ship target detection model based on YOLOv8s is proposed for lightweight SAR images.First,a lightweight residual feature enhancement module ACC,which uses adaptive pooling,is proposed to extract different contextual information.The residual enhancement method reduces the loss of feature information at the highest level in the feature pyramid,improves the expression of high-level features,and enhances the network's ability to detect small targets.Subsequently,lightweight dynamic snake-shaped convolution(DSConv)is introduced to replace the standard convolution operation,thereby improving detection performance of the model for small-strip ship targets and reducing missed target detection.Finally,the lightweight BiFormer dynamic sparse attention module is integrated to further optimize the small target detection effect.On the SAR ship detection dataset(SSDD),the algorithm proposed in this paper outperforms the original algorithm in precision,recall,and mAP@0.5,by 5.0,3.6,and 3.3 percentage points,respectively,and model detection ability for ship targets in SAR images is significantly enhanced.The generalized experimental results of the model on the high-resolution SAR images dataset(HRSID)show better performance than those of other classical algorithms.
关键词
舰船目标检测/SAR图像/残差增强/动态蛇形卷积/动态稀疏注意力Key words
ship target detection/SAR images/residual enhancement/dynamic snake shaped convolution/dynamic sparse attention引用本文复制引用
出版年
2024
NETL
NSTL
万方数据