Real-time Robust Lane Detection Method at a Speed of 100 km/h for a Vehicle-mounted Tunnel Surface Inspection System

Hayakawa, Tetsuo; Moko, Yushi; Morishita, Kenta; Ishikawa, Masatoshi

doi:10.1109/sas.2019.8705966

2019 IEEE Sensors Applications Symposium (SAS) 2019

DOI: 10.1109/sas.2019.8705966

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Real-time Robust Lane Detection Method at a Speed of 100 km/h for a Vehicle-mounted Tunnel Surface Inspection System

Tetsuo Hayakawa

Yushi Moko

Kenta Morishita

et al.

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Cited by 2 publications

References 7 publications

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Tunnel Lining Surface Monitoring System Deployable at Maximum Vehicle Speed of 100 km/h Using View Angle Compensation Based on Self-Localization Using White Line Recognition

Hayakawa¹,

Moko²,

Morishita³

et al. 2022

JRM

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Vehicle-mounted inspection systems have been developed to perform daily inspections of tunnel lining. However, it is difficult to continuously scan images at the desired target angle with the camera’s limited visual field because the vehicle does not always maintain the same position in the car lane while it runs. In this study, we propose a calibration-free optical self-localization method based on white line recognition, which can stabilize the scanning angle and thus enable the scanning of the entire tunnel lining surface. Self-localization based on the present method has a processing time of 27.3 ms, making it valid for real-time view angle compensation inside tunnels, where global positioning systems cannot operate. It is robust against changes in brightness at tunnel entrances and exits and capable of dealing with car lanes with dotted lines and regular white lines. We confirmed that it is possible to perform self-localization with 97.4% accuracy using the scanned data of white lines captured while the vehicle is running. When this self-localization was combined with an inspection system capable of motion blur correction, we were able to sequentially scan the tunnel lining surface at a stable camera angle at high speed, and it was shown that the acquired images could be stitched together to produce an expansion image that can be used for inspection.

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Tunnel Lining Surface Monitoring System Deployable at Maximum Vehicle Speed of 100 km/h Using View Angle Compensation Based on Self-Localization Using White Line Recognition

Hayakawa¹,

Moko²,

Morishita³

et al. 2022

JRM

View full text Add to dashboard Cite

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Vehicle Self-Position Estimation Using Lighting Recognition in Expressway Tunnel for Visual Inspection Flow

Moko,

Hiruma,

Hayakawa

et al. 2024

J. Robot. Mechatron.

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In this study, a stable and high-speed vision-based self-position estimation method was proposed that improves upon the existing method of lane detection by recognizing the lighting facilities that are installed in tunnels on Japanese expressways where GNSS cannot be used. In addition, we proposed a method for inspecting multiple cracks at once by estimating the self-position with the successful rate 75% in the traveling direction by counting the lighting with the successful rate 99.85%. The effectiveness of the method was verified by capturing images of cracks in an actual tunnel. The proposed method will enable more frequent inspections for tunnel cracks that lead to flaking while maintaining infrastructure safety, reducing costs, and improving tunnel visual inspection flow efficiency.

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Real-time Robust Lane Detection Method at a Speed of 100 km/h for a Vehicle-mounted Tunnel Surface Inspection System

Cited by 2 publications

References 7 publications

Tunnel Lining Surface Monitoring System Deployable at Maximum Vehicle Speed of 100 km/h Using View Angle Compensation Based on Self-Localization Using White Line Recognition

Tunnel Lining Surface Monitoring System Deployable at Maximum Vehicle Speed of 100 km/h Using View Angle Compensation Based on Self-Localization Using White Line Recognition

Vehicle Self-Position Estimation Using Lighting Recognition in Expressway Tunnel for Visual Inspection Flow

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