基于改进YOLOv5的船舶多尺度SAR图像检测算法  

Multi-scale SAR Image Detection Algorithm for Ships Based on Improved YOLOv5

作　　者：李生辉 李晓飞[1] 宋璋晗 王必祥 LI Shenghui;LI Xiaofei;SONG Zhanghan;WANG Bixiang(Engineering Research Center of the Ministry of Education for Broadband Wireless Communication Technology,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)

机构地区：[1]南京邮电大学宽带无线通信技术教育部工程研究中心,南京210003

出　　处：《数据采集与处理》2024年第1期120-131,共12页Journal of Data Acquisition and Processing

基　　金：江苏省重点研发计划(BE2016001-4)。

摘　　要：针对复杂场景下合成孔径雷达(Synthetic aperture radar, SAR)图像船舶目标像素尺度差异大和船舶密集排列造成目标漏检的问题,提出一种基于改进YOLOv5的船舶多尺度SAR图像检测算法。对于YOLOv5的颈部网络,采用双向特征金字塔结构(Bi-directional feature pyramid network, BiFPN)提升网络多尺度特征融合能力,并在其自下而上的特征融合支路中,基于深度可分离卷积(Depthwise separable convolution, DSC)和通道MLP构建EC-MLP(Enhanced channel-MLP)模块,从而丰富语义信息,提供更充分的船舶目标上下文特征;引入全局注意力机制(Global attention mechanism, GAM),使网络对输入特征进行针对性提取并运算,减少网络的信息丢失;此外,使用SIoU损失函数进一步提高网络的训练收敛速度和检测精度。在SSDD和HRSID数据集上与其他8种方法(Faster R-CNN、Libra R-CNN、FCOS、YOLOv5s、PP-YOLOv2、YOLOX-s、PP-YOLOE-s和YOLOv7-tiny)进行对比实验。实验结果表明:改进后算法在SSDD数据集上的AP50达到了96.7%,在HRSID数据集上AP50达到了95.6%,优于对比方法。An multi‐scale synthetic aperture radar(SAR)image detection algorithm for ships based on improved YOLOv5 is proposed to address the large pixel scale difference of ship targets in complex scenes and missed detection caused by dense array of ships.For the neck network of YOLOv5,a bi‐directional feature pyramid network(BiFPN)is adopted to enhance the multi‐scale feature fusion ability of the network,and an enhanced channel‐MLP(EC‐MLP)module is constructed based on depthwise separable convolution(DSC)and channel MLP in its bottom‐up feature fusion branch to enrich semantic information and provide more sufficient ship target context features.The global attention mechanism(GAM)is introduced to enable the network to extract input features selectively and reduce information reduction.In addition,the SIoU loss function is used to further improve the training convergence speed and detection accuracy of the network.Comparative experiments with eight other methods(Faster R‐CNN,Libra R‐CNN,FCOS,YOLOv5s,PP‐YOLOv2,YOLOX‐s,PP‐YOLOE‐s and YOLOv7‐tiny)are conducted on SSDD and HRSID datasets.The experimental results show that the AP50 of the improved algorithm reaches 96.7%on SSDD and 95.6%on HRSID,which is superior to the comparison methods.

关 键 词：合成孔径雷达 船舶目标检测 双向特征金字塔网络 深度可分离卷积 全局注意力机制 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]