基于双分支多尺度融合网络的毫米波SAR图像多目标语义分割方法

A multi-target semantic segmentation method for millimetre wave SAR images based on a dual-branch multi-scale fusion network

在毫米波合成孔径雷达(SAR)安检成像违禁品的检测与识别中,存在着目标尺寸过小、目标被部分遮挡和多目标之间重叠等复杂情况,不利于违禁品的准确识别。针对这些问题,提出了一种基于双分支多尺度融合网络(DBMFnet)的违禁品检测方法。该网络使用Encoder-Decoder的结构,在Encoder阶段,提出一种双分支并行特征提取网络(DBPFEN)来增强特征提取;在Decoder阶段,提出一种多尺度融合模块(MSFM)来提高对目标的检测能力。实验结果表明,该方法的均交并比(mIoU)均优于现有的语义分割方法,降低了漏检与错检率。

There are several major challenges in the detection and identification of contraband in millimetre-wave synthetic aperture radar(SAR)security imaging:the complexities of small target sizes,partially occluded targets and overlap between multiple targets,which are not conducive to the accurate identification of contraband.To address these problems,a contraband detection method based on dual branch multiscale fusion network(DBMFnet)is proposed.The overall architecture of the DBMFnet follows the encoder-decoder framework.In the encoder stage,a dual-branch parallel feature extraction network(DBPFEN)is proposed to enhance the feature extraction.In the decoder stage,a multi-scale fusion module(MSFM)is proposed to enhance the detection ability of the targets.The experimental results show that the proposed method outperforms the existing semantic segmentation methods in the mean intersection over union(mIoU)and reduces the incidence of missed and error detection of targets.