光电工程, 2024, 50 (12): 230242, 网络出版: 2024-03-26
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
毫米波合成孔径雷达 违禁品检测 深度学习 语义分割 双分支多尺度融合网络 millimetre-wave synthetic aperture radar contraband detection deep learning semantic segmentation dual-branch multi-scale fusion network
摘要
Abstract
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.
丁俊华, 袁明辉. 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[J]. 光电工程, 2024, 50(12): 230242. Junhua Ding, Minghui Yuan. A multi-target semantic segmentation method for millimetre wave SAR images based on a dual-branch multi-scale fusion network[J]. Opto-Electronic Engineering, 2024, 50(12): 230242.