Smart Tunnel Inspection and Assessment using Mobile Inspection Vehicle, Non-Contact Radar and AI

Yasuda, Toru; Yamamoto, Hideki; Enomoto, Mami; Nitta, Yasushi

doi:10.22260/isarc2020/0190

Cited by 5 publications

(5 citation statements)

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“…Different inspection methods have their advantages, disadvantages, and scope of applications, which means relying solely on one technique typically leads to limited detection performance. To obtain more information and improve detection accuracy, researchers have made progress in multisensor fusion and the integration of detection methods [20,25,47,48,72]. Mobile vehicles and robots are the carriers of integrated detection methods such as visible light or infrared cameras, radar, laser scanners, and acoustic sensors to achieve automatic tunnel inspection.…”

Section: (B)mentioning

confidence: 99%

“…The system was able to photograph the lining at a maximum speed of 100 km/h and automatically detect and assess the cracks. Mobile inspection vehicles have a strong carrying capacity and integrate several pieces of equipment, such as line scan cameras, laser scanners, and radars, so they can detect different tunnel defects and have been widely used in field inspection [72].…”

Section: (B)mentioning

confidence: 99%

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Tunnel lining detection and retrofitting

Jiang

Wang

Zhang

et al. 2023

Automation in Construction

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Section: (B)mentioning

confidence: 99%

Section: (B)mentioning

confidence: 99%

Tunnel lining detection and retrofitting

Jiang

Wang

Zhang

et al. 2023

Automation in Construction

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“…This system can simultaneously obtain images of the tunnel lining and its 3D contour, with a depth accuracy of 0.5 mm and a longitudinal and transverse resolution of 1 mm. Toru et al [4], from Pacific Consultants Co., Ltd., Tokyo, Japan, proposed the MIMM-R tunnel detection system consisting of 20 cameras, which can operate at speeds of 50-70 km/h. When captured with a pixel resolution of 1.5 mm, the system can identify cracks wider than 0.3 mm.…”

Section: Introductionmentioning

confidence: 99%

High-Speed Rail Tunnel Panoramic Inspection Image Recognition Technology Based on Improved YOLOv5

Duan

Qiu

Jin

et al. 2023

Sensors

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In order to meet the fast and accurate automatic detection requirements of equipment maintenance in railway tunnels in the era of high-speed railways, as well as adapting to the high dynamic, low-illumination imaging environment formed by strong light at the tunnel exit, we propose an automatic inspection solution based on panoramic imaging and object recognition with deep learning. We installed a hyperboloid catadioptric panoramic imaging system on an inspection vehicle to obtain a large field of view as well as to shield the high dynamic phenomena at the tunnel exit, and proposed a YOLOv5-CCFE object detection model based on railway equipment recognition. The experimental results show that the mAP@0.5 value of the YOLOv5-CCFE model reaches 98.6%, and mAP@0.5:0.95 reaches 68.9%. The FPS value is 158, which can meet the automatic inspection requirements of railway tunnel equipment along the line and has high practical application value.

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“…Therefore, vehicle-mounted air-coupled GPR, which offers advantages such as rapid, long-distance, and continuous inspection, has gradually been applied in tunnel inspections. However, current applications are mainly focused on road tunnels [14][15][16] and pavement inspections [17] using high-frequency antennas that cannot meet the requirements for the lining detection depth. The railway vehicle-mounted GPR detection system developed by the research team led by Professor Zan Yuewen at Southwest Jiaotong University achieved some success in detecting lining defects over long distances [18,19].…”

Section: Introductionmentioning

confidence: 99%

Application of Air-Coupled Ground Penetrating Radar Based on F-K Filtering and BP Migration in High-Speed Railway Tunnel Detection

Lei

Jiang

et al. 2023

Sensors

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As the number and length of high-speed railway tunnels increase in China, implicit defects such as insufficient lining thicknesses, voids, and poor compaction have become increasingly common, posing a serious threat to train operation safety. It is, therefore, imperative to conduct a comprehensive census of the defects within the tunnel linings. In response to this problem, this study proposes a high-speed railway tunnel detection method based on vehicle-mounted air-coupled GPR. Building on a forward simulation of air-coupled GPR, the study proposes the F-K filtering and BP migration algorithms based on the practical considerations of random noise and imaging interference from the inherent equipment. Through multi-dimensional quantitative comparisons, these algorithms are shown to improve the spectrum entropy values and instantaneous amplitude ratios by 4.6% and 11.6%; and 120% and 180%, respectively, over the mean and bandpass filtering algorithms, demonstrating their ability to suppress clutter and enhance the internal signal prominence of the lining. The experimental results are consistent with the forward simulation trends, and the verification using the ground-coupled GPR detection confirms that air-coupled GPR can meet the requirements of high-speed railway tunnel lining inspections. A comprehensive GPR detection model is proposed to lay the foundation for a subsequent defect census of high-speed railway tunnels.

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Smart Tunnel Inspection and Assessment using Mobile Inspection Vehicle, Non-Contact Radar and AI

Cited by 5 publications

References 0 publications

Tunnel lining detection and retrofitting

Tunnel lining detection and retrofitting

High-Speed Rail Tunnel Panoramic Inspection Image Recognition Technology Based on Improved YOLOv5

Application of Air-Coupled Ground Penetrating Radar Based on F-K Filtering and BP Migration in High-Speed Railway Tunnel Detection

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