Road detection and three-dimensional visualization management using deep learning and BIM integration

Qian Liu; Yinhao Ye

doi:10.1117/12.2684165

1 August 2023 Road detection and three-dimensional visualization management using deep learning and BIM integration

Qian Liu, Yinhao Ye

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127542Y (2023) https://doi.org/10.1117/12.2684165
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Asphalt pavement detection is a problem characterized by low efficiency, high manual input, and high cost. A 3D visualization management platform for road infrastructures based on artificial intelligence and information technology is presented in this study. Initially, images of cracks and potholes were processed digitally and segmented using image processing methods. A pavement condition index (PCI) is calculated by combining pavement distress identification with cracks and potholes. After that, ground penetrating radar (GPR) was used to detect the road structures, and then Canny edge algorithm was performed to process the original GPR images. Cracks, looseness, and poor interlayering were identified with the object detection model and data enhancement methods. Inverse time migration (3D) imaging of the internal diseases was performed in three dimensions (3D). Based on the results of the object detection model, F1 score and mAP were both above 80% for road distress detection. As a final step, a BIM model of asphalt pavement was generated using Revit software, and 3D asphalt pavement model rendering and quality details were displayed in Web-BIM browser, suggesting that this method can be used to maintain highways in a fast, intelligent, and scientific manner.

Citation Download Citation

Qian Liu and Yinhao Ye "Road detection and three-dimensional visualization management using deep learning and BIM integration", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127542Y (1 August 2023); https://doi.org/10.1117/12.2684165

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