Application and validation of simple image-mosaic technology for interpreting cracks on tunnel lining

Lee, Chia-Han; Chiu, Y.C.; Wang, Tai‐Tien; Huang, Tsan‐Hwei

doi:10.1016/j.tust.2012.11.002

Cited by 47 publications

(21 citation statements)

References 12 publications

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“…Inspired by [3][4] and [7], in this paper, we proposed a new concrete crack detect algorithm based on the local area sequential image mosaicking detection. Under this condition, we deeply give the study of the different character points detection algorithms and the matching stability of the sequential images.…”

Section: Figure 1 the Global And Local Concrete Imagesmentioning

confidence: 99%

“…Aimed at acquired the large area of the concrete surface, Li, Gang etc., [3] using the image mosaic algorithm to evaluate the reconstruction of the concrete pavement surface. In [4], Chia-Han Lee etc., give a study of the validation of simple image-mosaic technology for interpreting cracks on tunnel lining. According to their study, on the laboratory-scale tunnel condition, the mosaicked layout from vertical images is about1 .6 with sufficient image resolution for detecting lining cracks with widths exceeding 0.45mm.…”

Section: Related Workmentioning

confidence: 99%

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A New Stable and Accurate Algorithm of Concrete Crack Image Mosaic

Cai¹,

Liu²,

Wang³

et al. 2016

IJSIP

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Section: Figure 1 the Global And Local Concrete Imagesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A New Stable and Accurate Algorithm of Concrete Crack Image Mosaic

Cai¹,

Liu²,

Wang³

et al. 2016

IJSIP

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show abstract

“…However, they are also more likely to be considered hazardous to human health, since they may not be designed to carry humans, and may be more variable in size. Hence lighter, more flexible systems such as [7,8,11,12], and unmanned systems such as [9,10] are more suitable.…”

Section: Existing Technologymentioning

confidence: 99%

“…Fisheye systems are suitable for very low diameter tunnels or pipelines, but resolution and lighting become problematic at larger scales. The potential of a simple DSLR for accurate image mosaicing is shown in [11], but the technique is highly manual, requiring specified reference points or laser markers and assuming a precise geometry. A similar, more automated system is presented in [12] using a rig of DSLR cameras, but the capture process is manual.…”

Section: Existing Technologymentioning

confidence: 99%

“…Each quadrilateral is normalised to account for rig lighting, reducing visible seams in the final mosaic ( Figure 11). Because of the dense camera sampling and the fact that local planarity holds true for most of the tunnel surface, the result is of suitably high quality for inspection, while also being relatively fast to compute and not requiring the manual identification of control points as in [11] or parametric surface modelling [12]. …”

Section: Image Mosaicing For Condition Inspectionmentioning

confidence: 99%

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A Low-Cost Robotic System for the Efficient Visual Inspection of Tunnels

Stent¹,

Girerd²,

Long³

et al. 2015

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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We introduce a low-cost robotic system designed to enable the safe, objective and efficient visual inspection of tunnels. The system captures high resolution images and processes them to produce maps of tunnel linings that are suitable for detailed inspection.It is unique in that the total cost of hardware is an order of magnitude less than most existing systems while producing an equivalent or higher quality of output. The device makes use of consumer-grade digital cameras and high-power LEDs in a rotating rig, carried by a lightweight aluminium frame which is designed to reduce vibrations during data capture. It is portable and installable by hand and has a modular design, making it possible to adapt to different types of carriage units, tunnels and sensors.Within the paper, we share insight into features of the device's design, including lessons learned from trials of earlier prototypes and comparisons with alternative systems. Using field data gathered from a 2km utility tunnel, we demonstrate the use of our system as a means of visualising tunnel conditions through image mosaicing, cataloguing tunnel segments using barcode detection and improving the objectivity of visual condition surveys over time by the detection of sub-mm crack growth.We believe that our device is the first to provide comprehensive survey-quality data at such a low cost, making it very attractive as a tool for the improved visual monitoring of tunnels.

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Deep learning‐based classification and instance segmentation of leakage‐area and scaling images of shield tunnel linings

Zhao

Shadabfar

Zhang

et al. 2021

Struct Control Health Monit

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This paper presents an approach for the integrated process of classification and instance segmentation of leakage-area and scaling images from shield tunnel linings. For this purpose, the previously established dataset of leakage-area images by the authors is enlarged by means of adding scaling ones. Afterwards, data augmentation is implemented to enrich the database in the classification dataset, and the augmented classification dataset contains 5776 images. The instance segmentation dataset is subsequently enlarged through original images without any data augmentation, including 1496 images. Then a residual net with 101 layers (i.e., ResNet-101) is applied to the classification dataset to obtain a model that can identify leakage-area and scaling images from those of shield tunnel linings. The ResNet-101 classification model achieves an accuracy of 93.37% in terms of testing classification dataset. Moreover, a mask region-based convolutional neural network (Mask R-CNN) is utilized to perform instance segmentation of leakage areas and scaling in the images classified by the ResNet-101 model. The segmentation results of the Mask R-CNN model show 96.1% and 95.6% average precision (AP) with intersection over union (IoU) of 0.5 for bounding box and mask predication, respectively. By using the proposed approach, the leakage-area and scaling defects can be automatically classified and quantified with an overall accuracy of 89.3%, which is quite promising compared to the inherent uncertainty in geotechnical engineering. Image mosaicing is finally applied to provide inspectors with the intuitive observation of the location and distribution information of defects on the tunnel lining.

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Application and validation of simple image-mosaic technology for interpreting cracks on tunnel lining

Cited by 47 publications

References 12 publications

A New Stable and Accurate Algorithm of Concrete Crack Image Mosaic

A New Stable and Accurate Algorithm of Concrete Crack Image Mosaic

A Low-Cost Robotic System for the Efficient Visual Inspection of Tunnels

Deep learning‐based classification and instance segmentation of leakage‐area and scaling images of shield tunnel linings

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