[1]钟燕辉,邵艳浩,张瑞丰,等.基于三维探地雷达多视图融合的道路隐蔽裂缝识别[J].郑州大学学报(工学版),2027,48(XX):1-8.[doi:10.13705/j. issn.1671-6833.2027.01.005]
　ZHONG Yanhui,SHAO Yanhao,ZHANG Ruifeng,et al.Recognition of Road Subsurface Cracks in Pavement Based on 3D Ground Penetrating Radar Multi-view Fusion[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-8.[doi:10.13705/j. issn.1671-6833.2027.01.005]
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基于三维探地雷达多视图融合的道路隐蔽裂缝识别()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
48
期数:
2027年XX
页码:
1-8
栏目:
出版日期:
2027-12-10

文章信息/Info

Title:
Recognition of Road Subsurface Cracks in Pavement Based on 3D Ground Penetrating Radar Multi-view Fusion
作者:
钟燕辉1，邵艳浩1，张瑞丰2，李清波2，王 翔1，张 蓓1
1.郑州大学 水利与交通学院,河南 郑州 450001;2.河南交通投资集团有限公司开封分公司,河南 开封 475000
Author(s):
ZHONG Yanhui1, SHAO Yanhao1, ZHANG Ruifeng2, LI Qingbo2, WANG Xiang1, ZHANG Bei1
1. School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China; 2. Kaifeng Branch, Henan Communications Investment Group Co., Ltd, Kaifeng 475000, China
关键词:
探地雷达; 道路裂缝; 多视图融合; 注意力机制; 深度学习
Keywords:
ground penetrating radar;  pavement crack;  multi-view fusion;  attention mechanism;  deep learning
分类号:
TP391.41 U416.217
DOI:
10.13705/j. issn.1671-6833.2027.01.005
文献标志码:
A
摘要:
针对现有方法多依赖单视图雷达图像,忽视三维探地雷达多视图互补信息的问题,构建一种基于多视图融合与深度学习的道路隐蔽裂缝识别方法。 采用 gprMax 软件建立沥青路面三维正演模型,获取 B-scan、C-scan 和D-scan 这 3 个正交方向的雷达 剖 面,结 合 正 演 模 拟 与 实 测 数 据 构 建 了 包 含 3 437 组 三 视 图 样 本 的 数 据 集。 以DenseNet121 和 ResNet50 为主干网络,引入 SE 通道注意力机制,构建三分支并行结构,设计视图间注意力融合模块实现多视图特征的自适应加权融合。 实验结果表明,DenseNet121-TB 模型准确率、精确率、召回率和 F1 分别达到98. 60%、98. 71%、98. 60%和 98. 65%,较最优单视图模型准确率提升 4. 19 百分点。 消融实验验证了各模块对性能的正向贡献。 现场钻孔取芯验证表明,三视图特征与取芯结果具有良好对应关系,所提方法具有一定工程适用性。
Abstract:
To address the problem that existing methods rely predominantly on single-view radar images while ignoring the complementary multi-view information of three-dimensional ground penetrating radar, a road subsurface crack identification method based on multi-view fusion and deep learning was developed. A three-dimensional forward model of asphalt pavement was established using gprMax to obtain B-scan, C-scan, and D-scan radar profiles from three orthogonal directions, and a dataset comprising 3 437 triple-view samples was constructed by combining forward simulation with field measurement data. DenseNet121 and ResNet50 were adopted as backbone networks, with a Squeeze-and-Excitation channel attention mechanism introduced, a triple-branch parallel structure constructed, and an inter-view attention fusion module designed for adaptive weighted fusion of multi-view features. Experimental results showed that the DenseNet121-TB model achieved accuracy, precision, recall, and F1-score of 98.60%, 98.71%, 98.60%, and 98.65%, respectively, with accuracy 4.19 percentage points higher than that of the best single-view model. Ablation experiments verified the positive contribution of each module to recognition performance. Field core drilling validation demonstrated good correspondence between the three-view features and the drilling results, indicating that the proposed method has practical engineering applicability.

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更新日期/Last Update: 2026-10-30