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

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%.

Keywords

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

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Funding

National Natural Science Foundation of China(52338011)

National Natural Science Foundation of China(52378291)

Young Elite Scientists Sponsorship Program by CAST(2022-2024QNRC0101)