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

Li, Yonglong; Zhang, Hua; Wang, Shuang; Wang, Haoran; Li, Jialong

doi:10.1155/2019/6924976

Cited by 9 publications

(3 citation statements)

References 40 publications

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“…The device combined an elastomer-enabled contact-based optic sensor with specially designed artificial illumination to provide high-resolution and highquality pictures of the structural damage to the surface in a turbid water environment. Finally, the paper [45] introduced the UIS-1 underwater inspection system, comprising a proprietary underwater robot and a unique quantitative analysis approach. The technology was tested in the field at the dam in Sichuan, China, and its efficacy was compared to that of other approaches.…”

Section: Deep-learning-based Methods: Image Analysis Of Cracks Using ...mentioning

confidence: 99%

Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem

et al. 2023

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This paper presents the development of an underwater crack detection system for structural integrity assessment of submerged structures, such as offshore oil and gas installations, underwater pipelines, underwater foundations for bridges, dams, etc. Our focus is on the use of machine-learning-based approaches. First, a detailed literature review of the state of the current methods for underwater surface crack detection is presented, highlighting challenges and opportunities. An overview of the image augmentation approach for the creation of underwater optical effects is also presented. Experimental results using a standard network-based machine learning approach, which is used for surface crack detection in onshore environments, are presented. A series of test cases is presented in which existing networks’ performance is improved using augmented images for underwater conditions. The effectiveness and accuracy of the proposed approach in detecting cracks in underwater concrete structures are demonstrated. The proposed approach has the potential to improve the safety and reliability of underwater structures and prevent catastrophic failures.

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Section: Deep-learning-based Methods: Image Analysis Of Cracks Using ...mentioning

confidence: 99%

Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem

et al. 2023

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“…e bottom outlet stilling basin is a rectangular structure surrounded by the left guide wall, the middle guide wall, the end sill, and the apron. e top elevation of apron is 354.0 m, the basin length is 187.7 m, and the basin width is 75 m. e end sill of the stilling basin is continuous, and the elevation of the sill is 367 m. e antiscour section of the end sill is 35 m in length and 360.3 m in elevation [35][36][37]. e plan and the picture of surface outlet stilling basin and bottom outlet stilling basin are shown in Figure 1 and Figure 2, respectively.…”

Section: Project Backgroundmentioning

confidence: 99%

Analysis of Underwater Topographic Survey of Stilling Basin Based on Unmanned Survey System

Xie

Wang

Yang³

et al. 2021

Advances in Materials Science and Engineering

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The combination of Global Positioning System-Real Time Kinematic (GPS-RTK) and depth sounder is an important technical method of modern underwater topographic survey. In this paper, the combined technology was integrated with unmanned surface vehicle (USV) technology to construct an unmanned survey system suitable for underwater topographic survey in stilling basin, and it was applied to survey the underwater topography of the stilling basin of Tingzikou hydrojunction project. Based on the surveying and mapping data and 3D model, the trend of elevation change of the stilling basin including apron, end sill, antiscour section, and river convergence section was analyzed. The results show that, for the surface outlet stilling basin of Tingzikou, after flood, the structure is complete, the boundaries are clear, the water quality is good, and there is little sediment on the surface of apron from overflow dam section to end sill. Furthermore, no obvious sediment or structural abnormality has been found in the still basin. Affected by the bedrock and cofferdam cobble gravel sediment, the elevation of the antiscour section and the downstream convergence section of the surface outlet stilling basin increased significantly. For the bottom outlet stilling basin of Tingzikou, there is large sediment within a range of 20 m∼40 m from the bottom sill and the maximum height of it reaches 2.04 m, while there is no obvious sediment or structural abnormality in the remaining region of basin. The critical sedimentation height can effectively judge the safety grade of the sedimentation height in stilling basin and provide a reliable support for assessing the overall safety of stilling basin. The application of unmanned survey technology improves the accuracy and timeliness of underwater topography and sedimentation distribution of stilling basin, which has significant application research value and promotion significance.

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“…In [16,17], CNN-based methods are applied to the same set of 40,000 images of 227 × 227 resolution, both claiming good prediction accuracy of over 99% and 90% respectively. Differently from the previous examples, in [18], the authors have used 60,000 images of a very high resolution (3120 × 4160), for training and testing of the applied CNN-based crack identification method with a very high prediction accuracy of about 99%.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Challenges Associated with Automatic Detection of Concrete Cracks in the Presence of Shadows

et al. 2021

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Detection and assessment of cracks in civil engineering structures such as roads, bridges, dams and pipelines are crucial tasks for maintaining the safety and cost-effectiveness of those concrete structures. With the recent advances in machine learning, the development of ANN- and CNN-based algorithms has become a popular approach for the automated detection and identification of concrete cracks. However, most of the proposed models are trained on images taken in ideal conditions and are only capable of achieving high accuracy when applied to the concrete images devoid of irregular illumination conditions, shadows, shading, blemishes, etc. An overview of challenges related to the automatic detection of concrete cracks in the presence of shadows is presented in this paper. In particular, difficulties associated with the application of deep learning-based methods for the classification of concrete images with shadows are demonstrated. Moreover, the limitations of the shadow removal techniques for the improvement of the crack detection accuracy are discussed.

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

Cited by 9 publications

References 40 publications

Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem

Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem

Analysis of Underwater Topographic Survey of Stilling Basin Based on Unmanned Survey System

An Overview of Challenges Associated with Automatic Detection of Concrete Cracks in the Presence of Shadows

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