船舶与海洋工程

复杂场景下的SAR图像船舶目标检测

  • 周慧 ,
  • 褚娜 ,
  • 陈澎
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  • (1. 大连东软信息学院 计算机与软件学院, 辽宁 大连 116023;2 .大连海事大学 航海学院, 辽宁 大连 116026)
周慧(1983 — ),女,副教授,E-mail:zhouhui@neusoft.edu.cn.

收稿日期: 2020-04-01

  修回日期: 2020-05-12

  网络出版日期: 2020-05-12

基金资助

国家自然科学基金资助项目 (51609032);辽宁省教育厅科学研究项目.

Ship target detection of SAR images in complex scenes

  • ZHOU Hui ,
  • CHU Na ,
  • CHEN Peng
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  • (1.School of Computer and Software, Dalian Neusoft Information University, Dalian 116023, China;2.Navigation College, Dalian Maritime University, Dalian 116026, China)

Received date: 2020-04-01

  Revised date: 2020-05-12

  Online published: 2020-05-12

摘要

针对SAR图像中复杂场景下的船舶目标检测问题,提出一种将定位、分类和分割多任务结合一起的Mask-FPN模型,在金字塔特征映射图的基础上,同时引入图像分割分支,并通过多任务损失函数保证定位分类和分割三个过程同时进行.实验结果表明,本文提出的Mask-FNP模型在近海、港口、岛屿等存在干扰背景的复杂场景下,船舶识别准确率达98.81%.与Faster R-CNN、Yolo3、 SSD、FPN等模型对比,本文模型检测准确率提高,虚警率和漏检率明显下降.

本文引用格式

周慧 , 褚娜 , 陈澎 . 复杂场景下的SAR图像船舶目标检测[J]. 大连海事大学学报, 2020 , 46(3) : 87 -94 . DOI: 10.16411/j.cnki.issn1006-7736.2020.03.010

Abstract

Aiming at the problem of ship target detection in complex scene of SAR image, a Mask-FPN model combining localization, classification and segmentation multitasking was proposed. Based on pyramid feature map, the image segmentation branches were introduced at the same time, and multi task loss function was used to ensure that the three processes as positioning, classification and segmentation were carried out simultaneously. The experimental results show that the accuracy of the Mask-FNP model is up to 98.81% in the complex scenes with interference background such as offshore, port and island. Compared with Faster R-CNN, Yolo3, SSD, FPN and other models, the detection accuracy of this model is higher, and the false alarm rate and missing detection rate are significantly reduced.

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