《计算机应用》唯一官方网站 ›› 2022, Vol. 42 ›› Issue (4): 1275-1283.DOI: 10.11772/j.issn.1001-9081.2021071263

• CCF第36届中国计算机应用大会 (CCF NCCA 2021) • 上一篇    

基于自适应双阈值的地下目标自动检测算法

李海丰(), 赵碧帆, 侯谨毅, 王怀超, 桂仲成   

  1. 中国民航大学 计算机科学与技术学院,天津 300300
  • 收稿日期:2021-05-15 修回日期:2021-08-26 接受日期:2021-08-30 发布日期:2021-08-26 出版日期:2022-04-10
  • 通讯作者: 李海丰
  • 作者简介:赵碧帆(1996—),女,河南辉县人,硕士研究生,主要研究方向:图像处理、计算机视觉
    侯谨毅(1987—),男,河南林州人,讲师,博士,主要研究方向:计算机视觉
    王怀超(1984—),男,天津人,讲师,博士,主要研究方向:计算机视觉
    桂仲成(1979—),男,安徽六安人,教授级高级工程师,主要研究方向:智能机器人。
  • 基金资助:
    国家重点研发计划项目(2019YFB1310601)

Automatic detection algorithm for underground target based on adaptive double threshold

Haifeng LI(), Bifan ZHAO, Jinyi HOU, Huaichao WANG, Zhongcheng GUI   

  1. College of Computer Science and Technology,Civil Aviation University of China,Tianjin 300300,China
  • Received:2021-05-15 Revised:2021-08-26 Accepted:2021-08-30 Online:2021-08-26 Published:2022-04-10
  • Contact: Haifeng LI
  • About author:ZHAO Bifan, born in 1996, M. S. candidate. Her research interests include image processing, computer vision.
    HOU Jinyi, born in 1987, Ph. D., lecturer. His research interests include computer vision.
    WANG Huaichao, born in 1984, Ph. D., lecturer. His research interests include computer vision.
    GUI Zhongcheng, born in 1979, professor level senior engineer. His research interests include intelligent robot.
  • Supported by:
    National Key Research and Development Program of China(2019YFB1310601)

摘要:

在使用探地雷达(GPR)生成的Bscan图像进行地下目标检测时,当前基于深度学习的目标检测网络模型存在训练样本需求量高、耗时长,不能区分目标显著程度,难以识别复杂目标等问题。针对以上问题,提出一种基于直方图的双阈值分割算法。首先,根据地下目标的GPR图像直方图分布特性,快速从直方图中计算出分割地下目标所需的两个阈值;然后,采用支持向量机(SVM)和LeNet的组合分类器模型对分割结果进行分类识别;最后,进行分类结果整合并统计精确度数值。相较于传统的最大类间方差法(Ostu)、迭代法等阈值分割算法,所提算法获得的地下目标分割结果结构更加完整,并且几乎不含噪声。在真实数据集上,所提算法的平均识别准确率达到了90%以上,比仅使用单一分类器的平均识别准确率提高40%以上。实验结果表明,所提算法能够同时有效分割显著和非显著性地下目标,且采用的组合分类器能够获得更好的分类结果,适用于小样本数据集的地下目标自动检测和识别。

关键词: 探地雷达, 图像处理, 目标分割, 目标检测, 双阈值方法, 组合分类器

Abstract:

When using the Bscan image generated by Ground Penetrating Radar (GPR) to detect underground targets, the current target detection network models based on deep learning have some problems, such as high demand of training samples, long time consuming, unable to distinguish the significance of targets, and difficult to identify complex targets. To solve the above problems, a double threshold segmentation algorithm based on histogram was proposed. Firstly, based on the distribution characteristics of GPR image histogram of underground target, two thresholds for underground target segmentation were calculated quickly from the histogram. Then, a combination classifier model with Support Vector Machine (SVM) and LeNet was used to classify the segmentation results. Finally, classification results were integrated and the accuracy values were counted. Compared with the traditional threshold segmentation algorithms such as Ostu and iterative methods, the structure of the underground target segmentation results obtained by the proposed algorithm was more complete and almost free of noise. On the real dataset, the average recognition accuracy of the proposed algorithm reached more than 90%, which was more than 40% higher than that of the algorithm using a single classifier. The experimental results show that the salient and non-salient underground targets can be effectively segmented at the same time, and the combination classifier can obtain better classification results. It is suitable for automatic detection and recognition of underground targets with small sample datasets.

Key words: Ground Penetrating Radar (GPR), image processing, target segmentation, target detection, double threshold method, combination classifier

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