序号 | 参考文献 | 时间 | GPR目标 | 客观评价 |
1 | Borgioli et al. [17] | 2008 | 地埋管道 | 在Hough变换中引入加权因子,解决了管道靠近时双曲线重叠的问题;但是需要预备模型,计算成本相对较高。 |
2 | Maas et al. [23] | 2013 | 双曲线反射 | 使用Viola-Jones算法标记目标候选区域,它避免了模板匹配并缩小了后续搜索区域;然而,应用特征需手动识别,分类结果取决于特征的质量,难度随着数据量的增加。 |
3 | Besaw et al. [2] | 2016 | 地埋爆炸物 | 应用CNN从GPR B-scan中提取有意义的特征并对目标进行分类。交叉验证,网络权重正则化和“dropout”用于防止过度训练。 |
4 | Bishop et al. [3] | 2016 | 地埋爆炸物 | 在CNN基础上增加了额外的Data Augmentation技术,用于增加可用训练数据的数量和可变性。 |
5 | Daniel et al. [4,5] | 2017 | 地埋爆炸物 | 研究了预训练CNN的初始化步骤,以解决GPR数据标记样本不足的问题;但是输入网络中真实图像的大小和数量通常是有限的,仅实现分类步骤。 |
6 | Pham et al. [27] | 2018 | 双曲线反射 | 首次采用Faster RCNN来检测GPR B-scan中的反射双曲线。该技术在真实测试集上的性能要超过使用HOG或Haar-like特征的检测器,但缺少定量的评估。 |
7 | Hou et al. [28] | 2019 | 地埋钢筋 | 在文献[27]基础上,采用了DA手段增加真实GPR数据集和仿真数据集;提出DCSE算法以识别双曲线特征,完善了文献[30]中提出的OSCA算法;提出CTFP算法自动提取拟合点。所提出方案的有效性在仿真和真实数据集上得到了验证。 |
8 | Doue et al. [29] | 2016 | 双曲线反射 | 提出了C3算法分割交叉双曲线,并将其送入神经网络进行分类。C3算法水平扫描B-scan图像中的每个像素以进行聚类。然而,双曲线是垂直向下打开的,C3算法没有考虑这个重要特征。 |
9 | Zhou et al. [30] | 2018 | 金属管道 水泥管道 | 提出OSCA算法解决了文献[29]中的难题,可以识别具有向下开口特征的聚类。然而,在整个图像上进行OSCA算法是不合适的,因为难以处理包含太多非平稳噪声的大型现场数据集,导致后续处理复杂化。 |

Citation: Feifei HOU, Ronghua SHI, Wentai LEI, Jian DONG, Mengdi XU, Jingchun XI. A Review of Target Detection Algorithm for GPR B-scan Processing[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT190680

面向GPR B-scan图像的目标检测算法综述
English
A Review of Target Detection Algorithm for GPR B-scan Processing
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[1]
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图 2 在文献[28]中解决的一些复杂情况示例
表 1 GPR目标检测的经典算法总结
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