Mobile Tunnel Lining Measurable Image Scanning Assisted by Collimated Lasers.

Area of Science:

• Civil Engineering
• Robotics
• Computer Vision

Background:

• Road tunnel lining health is critical for traffic safety, necessitating regular inspections.
• Current mobile scanning methods struggle with image stitching due to insufficient feature points, leading to complex seam elimination algorithms.
• Disrupting traffic for inspections is undesirable, making mobile scanning solutions essential.

Purpose of the Study:

• To propose a novel mobile scanning method for road tunnel linings that overcomes image stitching challenges.
• To develop a system that enables accurate measurement of tunnel lining appearance defects without traffic disruption.
• To improve the efficiency and accuracy of tunnel inspection processes.

Main Methods:

• A mobile scanning method utilizing collimated lasers as corresponding points for image stitching.
• Lasers are employed as structured light to measure image projection relationships.
• A checkerboard was used for system calibration, estimating laser and camera poses, supported by a Laplace kernel-based algorithm for accuracy.

Main Results:

• The proposed laser-assisted method significantly improves image stitching compared to existing techniques.
• The developed inspection car system produces nearly seamless and measurable tunnel lining images.
• The Laplace kernel-based calibration algorithm demonstrated superior performance over benchmark algorithms.

Conclusions:

• The proposed mobile scanning method using lasers is feasible and effective for road tunnel lining inspection.
• This approach addresses key limitations in current methods, offering a more robust solution for defect detection.
• The system enhances traffic safety by enabling efficient and accurate tunnel health monitoring.