[1] JIA Jingwei, NI Youhao, MAO Jianxiao, XU Yinfei, et al. Geometric parameter identification of bridge precast box girder sections based on deep learning and computer vision [J]. Journal of Southeast University (English Edition), 2025, 41 (3): 278-285. [doi:10.3969/j.issn.1003-7985.2025.03.003]

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Geometric parameter identification of bridge precast box girder sections based on deep learning and computer vision()

基于深度学习和计算机视觉的桥梁预制箱梁截面几何参数识别

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

41

Issue:

2025 3

Page:

278-285

Research Field:

Traffic and Transportation Engineering

Publishing date:

2025-09-11

Info

Title:

Geometric parameter identification of bridge precast box girder sections based on deep learning and computer vision

基于深度学习和计算机视觉的桥梁预制箱梁截面几何参数识别

Author(s):

JIA Jingwei, NI Youhao, MAO Jianxiao, XU Yinfei, WANG Hao

Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 211189, China

贾景伟, 倪有豪, 茅建校, 徐寅飞, 王浩

东南大学混凝土及预应力混凝土结构教育部重点实验室，南京211189

Keywords:

bridge precast components;  section geometry parameters;  size identification;  computer vision;  deep learning

桥梁预制构件; 截面几何参数; 尺寸识别; 计算机视觉; 深度学习

PACS:

U445.47;TP18

DOI:

10.3969/j.issn.1003-7985.2025.03.003

Abstract:

To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components, a method based on deep learning and computer vision is developed to identify the geometric parameters. The study utilizes a common precast element for highway bridges as the research subject. First, edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology. Subsequently, a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output. A dataset is generated by varying the control parameters and noise levels for model training. Finally, field measurements are conducted to validate the accuracy of the developed method. The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components, with an error rate maintained within 5%.

为解决现有桥梁预制构件几何参数测量方法存在的效率低、依赖人工问题，以某高速公路桥梁通用装配式预制构件为对象，基于深度学习和计算机视觉开展截面几何参数识别方法研究。首先，以所拍摄的桥梁预制构件横截面图像为对象，融合Canny算子和数学形态学方法，提取桥梁构件截面的边缘特征点。然后，以所提取图像的边缘坐标为输入，以预先确定的桥梁构件截面的控制参数为输出，建立用于预制构件横截面几何参数识别的深度学习模型，通过改变控制参数和噪声生成数据集，用于模型训练。最后，开展现场测试以验证所建立方法的精度。结果表明，建立的集成参数识别方法可以有效识别桥梁预制构件的截面几何参数，误差可控制在5%以内。

References:

[1]LUO Z, WANG Y H. Experimental and numerical investigations of the impact resistance of socket and pocket connections for precast bridge columns[J]. Structures, 2024, 64: 106631.
[2]CHENG J M, JIN H, ZHENG Z J, et al. Welding seam groove recognition of steel structure on construction site based on machine vision[J]. Journal of Southeast University (Natural Science Edition), 2023, 53(1): 86-93. (in Chinese)
[3]YE X T, ZHOU Y, GUO H L, et al. A computer vision-based approach to automatically extracting the aligning information of precast structural components[J]. Automation in Construction, 2024, 164: 105478.
[4]NI Y H, MAO J X, WANG H, et al. Surface damage detection and localization for bridge visual inspection based on deep learning and 3D reconstruction[J]. Structural Control and Health Monitoring, 2024, 2024(1): 9988793.
[5]NI Y H, LU H, JI C, et al. Comparative analysis on bridge corrosion damage detection based on semantic segmentation[J]. Journal of Southeast University (Natural Science Edition),2023, 53(2): 201-209. (in Chinese)
[6]JIANG Z X, SU R, LIU Y X, et al. Accurate relative measurement of multitarget poses by monocular vision for nonmodel-based real-time calibration of industrial robot[J]. Measurement, 2024, 235: 114979.
[7]WENG S, GUO J Z, YU H, et al. Synchronized identification of dynamic load magnitude and location based on convolutional neural network[J]. Journal of Southeast University (Natural Science Edition), 2024, 54(1): 110-116. (in Chinese)
[8]HE Z R, SHEN Q F, WU J X, et al. Transformer encoder-based multilevel representations with fusion feature input for speech emotion recognition [J]. Journal of Southeast University (English Edition), 2023, 39(1): 68-73.
[9]NI Y H, MAO J X, WANG H, et al. Toward high-precision crack detection in concrete bridges using deep learning[J]. Journal of Performance of Constructed Facilities, 2023, 37(3): 04023017.
[10]WANG Y J, LUO Y Z. Displacement measurement method of sphere joints in space structures based on machine vision and deep learning[J]. Spatial Structures, 2019, 25(4): 60-66, 42. (in Chinese)
[11]DANG Y, HE Y Z. Dynamic displacement measurement method for an isolation bearing based on computer vision and deep learning[J]. Journal of Vibration and Shock, 2023, 42(6): 90-97, 165. (in Chinese)
[12]KASANI H, BAVIL M A, ASHRAFI S, et al. High count rate alpha particle spectroscopy by a digital optical camera: A comparison study on edge detection algorithms[J]. Radiation Physics and Chemistry, 2024, 223: 112024.
[13]CHOW Y T, GANGBO W. A partial Laplacian as an infinitesimal generator on the Wasserstein space[J]. Journal of Differential Equations, 2019, 267(10): 6065-6117.
[14]LI M L, WANG Z W, LIU L T, et al. Subgraph-aware graph kernel neural network for link prediction in biological networks[J]. IEEE Journal of Biomedical and Health Informatics, 2024, 28(7): 4373-4381.
[15]YU Y F, YAN Y J. Color image hybrid noise filtering algorithm based on deep convolution neural network[J]. Systems and Soft Computing, 2024, 6: 200120.

Memo

Memo:

Received 2024-10-15,Revised 2024-12-16.
Biographies:Jia Jingwei(1998─),male, Ph.D. graduate;Mao Jianxiao(corresponding author), male, doctor, associate researcher, jianxiao@seu.edu.cn.
Foundation items:The National Natural Science Foundation of China (No. 52338011, 52378291), Young Elite Scientists Sponsorship Program by CAST (No. 2022-2024QNRC0101).
Citation:JIA Jingwei,NI Youhao,MAO Jianxiao,et al.Geometric parameter identification of bridge precast box girder sections based on deep learning and computer vision[J].Journal of Southeast University (English Edition),2025,41(3):278-285.DOI:10.3969/j.issn.1003-7985.2025.03.003.DOI:10.3969/j.issn.1003-7985.2025.03.003

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