A computer vision based rebar detection chain for automatic processing of concrete bridge deck GPR data

Asadi, Pouria; Gindy, Mayrai; Álvarez, Marco; Asadi, Abbas

doi:10.1016/j.autcon.2020.103106

Cited by 42 publications

(14 citation statements)

References 16 publications

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“…When dealing with a GPR bridge inspection, a large amount of data are produced and extensive manual processing is required to extract useful information, in which the manual identification and localization of rebar is time-consuming and labor intensive. In such a context, some authors have developed algorithms for the automatic detection of rebar [157,173,174,[176][177][178][179][180][181][182] and cracks/delamination [165,177,178].…”

Section: Concrete Bridgesmentioning

confidence: 99%

“…There is a large variety of computational methods that can be applied, such as interpolation functions, a histogram of oriented gradients (HOG), a feature descriptorsbased multi-layer perceptron (MLP) classifier, a support vector machine (SVM), a convolutional neural network (CNN), the limited and simplified hyperbolic summation (LSHS) technique, partial differential equations (PDEs), the hidden Markov model (HMM), a genetic algorithm, and the fuzzy logic approach. These computational methods are described and applied in some relevant and recent studies [177][178][179][180][181] and, in most cases, with accuracies greater than 95.0%. On the other hand, algorithms are also implemented to extract the GPR amplitudes automatically from both the whole GPR signals and the rebar peaks detected [165], [171], [174], generally after applying a migration procedure.…”

Section: Concrete Bridgesmentioning

confidence: 99%

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A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

2021

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The non-destructive testing and diagnosis of transport infrastructures is essential because of the need to protect these facilities for mobility, and for economic and social development. The effective and timely assessment of structural health conditions becomes crucial in order to assure the safety of the transportation system and time saver protocols, as well as to reduce excessive repair and maintenance costs. Ground penetrating radar (GPR) is one of the most recommended non-destructive methods for routine subsurface inspections. This paper focuses on the on-site use of GPR applied to transport infrastructures, namely pavements, railways, retaining walls, bridges and tunnels. The methodologies, advantages and disadvantages, along with up-to-date research results on GPR in infrastructure inspection are presented herein. Hence, through the review of the published literature, the potential of using GPR is demonstrated, while the main limitations of the method are discussed and some practical recommendations are made.

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Section: Concrete Bridgesmentioning

confidence: 99%

Section: Concrete Bridgesmentioning

confidence: 99%

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

2021

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“…The detection of hyperbola region from B-scans is a first step of GPR data interpretation, after that, the parameters estimation of underground cylindrical objects can be conducted based on the picked regions [18]. In Reference [19], the Hough transform was first used for hyperbola detection in GPR B-scans.…”

Section: Introductionmentioning

confidence: 99%

Underground Cylindrical Objects Detection and Diameter Identification in GPR B-Scans via the CNN-LSTM Framework

et al. 2020

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Ground penetrating radar (GPR), as a non-invasive instrument, has been widely used in the civil field. The interpretation of GPR data plays a vital role in underground infrastructures to transfer raw data to the interested information, such as diameter. However, the diameter identification of objects in GPR B-scans is a tedious and labor-intensive task, which limits the further application in the field environment. The paper proposes a deep learning-based scheme to solve the issue. First, an adaptive target region detection (ATRD) algorithm is proposed to extract the regions from B-scans that contain hyperbolic signatures. Then, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework is developed that integrates Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to extract hyperbola region features. It transfers the task of diameter identification into a task of hyperbola region classification. Experimental results conducted on both simulated and field datasets demonstrate that the proposed scheme has a promising performance for diameter identification. The CNN-LSTM framework achieves an accuracy of 99.5% on simulated datasets and 92.5% on field datasets.

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“…The data processing in CV is done by acquiring an image, then extracting the information that needs to be processed. CV obtains the input images using a high-level approach; based on this approach, classifications are attained that render the appropriate results [11]. Thus, recognition methods are developed to detect the images.…”

Section: Introductionmentioning

confidence: 99%

A two-level computer vision-based information processing method for improving the performance of human–machine interaction-aided applications

Alfarraj

Tolba

2020

Complex Intell. Syst.

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The computer vision (CV) paradigm is introduced to improve the computational and processing system efficiencies through visual inputs. These visual inputs are processed using sophisticated techniques for improving the reliability of human–machine interactions (HMIs). The processing of visual inputs requires multi-level data computations for achieving application-specific reliability. Therefore, in this paper, a two-level visual information processing (2LVIP) method is introduced to meet the reliability requirements of HMI applications. The 2LVIP method is used for handling both structured and unstructured data through classification learning to extract the maximum gain from the inputs. The introduced method identifies the gain-related features on its first level and optimizes the features to improve information gain. In the second level, the error is reduced through a regression process to stabilize the precision to meet the HMI application demands. The two levels are interoperable and fully connected to achieve better gain and precision through the reduction in information processing errors. The analysis results show that the proposed method achieves 9.42% higher information gain and a 6.51% smaller error under different classification instances compared with conventional methods.

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A computer vision based rebar detection chain for automatic processing of concrete bridge deck GPR data

Cited by 42 publications

References 16 publications

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

A Review of GPR Application on Transport Infrastructures: Troubleshooting and Best Practices

Underground Cylindrical Objects Detection and Diameter Identification in GPR B-Scans via the CNN-LSTM Framework

A two-level computer vision-based information processing method for improving the performance of human–machine interaction-aided applications

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