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

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