A novel underwater dam crack detection and classification approach based on sonar images

Shi, Pengfei; Fan, Xinnan; Ni, Jianjun; Khan, Zubair; Li, Min

doi:10.1371/journal.pone.0179627

Cited by 32 publications

(15 citation statements)

References 31 publications

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“…e distance D between pixel i and j is simplified to the Eq. D � 2 is the spatial distance, and m is the importance parameter between the above two distances.…”

Section: Superpixel Algorithmmentioning

confidence: 99%

“…Not like other hydraulic structures, abrasion erosion which is caused by friction and impact of waterborne debris on the concrete surface is a major damage of stilling basin slabs [1]. ere are some traditional underwater methods used for dam erosion detection such as sonar [2,3] and ground-penetrating radar [4]. But the equipment of these methods is expensive, and the results of detection are not visual intuitive.…”

Section: Introductionmentioning

confidence: 99%

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Image‐Based Underwater Inspection System for Abrasion of Stilling Basin Slabs of Dam

Zhang

Wang

et al. 2019

Advances in Civil Engineering

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The abrasion of stilling basin slabs which is caused by waterborne particles is one of the main surface damages in the operation of hydropower station. For determining whether to repair the stilling basin slabs, periodic inspections of erosion condition of stilling basin slabs are required. The practical problem is how to get the underwater image without unwatering and how to analyse the abrasion though the images. This paper developed a novel underwater inspection system named UIS-1 which consists of a customized underwater robot and special quantitative analysis method for this situation. Firstly, the integrated component was designed for the underwater robot that partially removes the siltation and obtains the image of the concrete surface of stilling basin slabs in the desired position. Secondly, the paper proposed an image algorithm to obtain aggregate exposure ratio for quantitative abrasion analysis. This image algorithm used SLIC superpixel and the SVM machine learning method to detect the coarse aggregate exposure automatically. Then, the aggregate exposure ratio was calculated to analyse the degree of abrasion. Finally, the UIS-1 system was evaluated in the field experiments of a dam in Sichuan, China, and its performance was discussed by comparison.

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“…e distance D between pixel i and j is simplified to the Eq. D � 2 is the spatial distance, and m is the importance parameter between the above two distances.…”

Section: Superpixel Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image‐Based Underwater Inspection System for Abrasion of Stilling Basin Slabs of Dam

Zhang

Wang

et al. 2019

Advances in Civil Engineering

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“…When detecting defects under uneven illumination conditions, if the same threshold detection is used for strong and weak illumination, it will inevitably lead to missed detection and false detection. Moreover, this phenomenon also occurs when cracks are detected in images captured in the areas of underwater dams [ 1 , 2 , 3 ], highway pavements [ 4 , 5 , 6 ], and bridges [ 7 , 8 , 9 ]. When the width and number of cracks exceed the allowable range, it will lead to structural decay and affect compressive strength variation [ 10 ], structural response, and seismic fragility [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Partition Gaussian Crack Detection Algorithm Based on Projection Curve Distribution

Xue

Yuan

2020

Sensors

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When detecting the cracks in the tunnel lining image, due to uneven illumination, there are generally differences in brightness and contrast between the cracked pixels and the surrounding background pixels as well as differences in the widths of the cracked pixels, which bring difficulty in detecting and extracting cracks. Therefore, this paper proposes a dynamic partitioned Gaussian crack detection algorithm based on the projection curve distribution. First, according to the distribution of the image projection curve, the background pixels are dynamically partitioned. Second, a new dynamic partitioned Gaussian (DPG) model was established, and the set rules of partition boundary conditions, partition number, and partition corresponding threshold were defined. Then, the threshold and multi-scale Gaussian factors corresponding to different crack widths were substituted into the Gaussian model to detect cracks. Finally, crack morphology and the breakpoint connection algorithm were combined to complete the crack extraction. The algorithm was tested on the lining gallery captured on the site of the Tang-Ling-Shan Tunnel in Liaoning Province, China. The optimal parameters in the algorithm were estimated through the Recall, Precision, and Time curves. From two aspects of qualitative and quantitative analysis, the experimental results demonstrate that this algorithm could effectively eliminate the effect of uneven illumination on crack detection. After detection, Recall could reach more than 96%, and after extraction, Precision was increased by more than 70%.

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“…[2][3][4] Digital image processing algorithms, in which the length, width, shape, and even the depth of the cracks can be calculated from the acquired images, require fixed reference marks or reference planes, which in practice is hard to obtain and maintain. [5][6][7][8] Distributed fiber optic sensors, such as those based on Brillouin scattering techniques, that is, Brillouin optical time-domain reflectometer (BOTDR) and Brillouin optical time-domain analyzer (BOTDA), have gained widespread usage for monitoring of strain and temperature in a number of applications including in oil explorations, aeronautics, and civil structural systems. 9 Brillouin scattering occurs when light along the length of an optical fiber interacts with time-dependent density variations in the core of the optical fiber resulting in a frequency shift in the optical signal.…”

Section: Introductionmentioning

confidence: 99%

Visual saliency–based image binarization approach for detection of surface microcracks by distributed optical fiber sensors

Song

Oskoui

Taylor

et al. 2018

Structural Health Monitoring

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Early detection of defects and anomalies is important for safety and efficient management of structural elements. Brillouin scattering–based optical fiber sensors provide distributed sensing capabilities by monitoring the strain and temperature over large distances in structural elements. Their use has been limited to oil and gas explorations, mainly due to the inherent low signal-to-noise ratio in such systems, preventing detection of microcracks in structural monitoring applications. This study introduces a method based on the visual saliency approach through which the digital images acquired by the distributed strain data are employed for the detection of surface microcracks. When using this method, strain data sequences along the entire length of a structural element are sampled with a Brillouin scattering–based optical fiber sensor and then divided into a set of equal-length subsequences. A similarity measure matrix is composed based on the distributed strain data and then converted into a grayscale image. The saliency maps of the acquired grayscale images are calculated and a center-hollowed square template is defined and exploited for convolution with the binarized saliency map as a filter operator. The pixels retained after the filtering correspond to the locations of microcracks. Verification of the method was accomplished by experiments on a 15-m-long steel beam with fabricated defects.

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A novel underwater dam crack detection and classification approach based on sonar images

Cited by 32 publications

References 31 publications

Image‐Based Underwater Inspection System for Abrasion of Stilling Basin Slabs of Dam

Image‐Based Underwater Inspection System for Abrasion of Stilling Basin Slabs of Dam

Dynamic Partition Gaussian Crack Detection Algorithm Based on Projection Curve Distribution

Visual saliency–based image binarization approach for detection of surface microcracks by distributed optical fiber sensors

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