research-article

Dam surface crack detection based on deep learning

Authors:

Jishuang HuangAuthors Info & Claims

RICAI '19: Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence

Pages 738 - 743

https://doi.org/10.1145/3366194.3366327

Published: 20 September 2019 Publication History

Abstract

According to the statistics of the First National Water Census Bulletin in 2013[1], the number of water conservancy projects in China has exceeded 98,000, and 756 are under construction, with a total storage capacity of more than 930 billion cubic meter, ranking first in the world. While these water conservancy projects bring enormous economic and social benefits to China, they are affected by geology, hydrology, meteorology and other factors, and their buildings such as tunnels are prone to various defects. However, the current methods for detecting cracks on the dam surface are still dominated by humans. This process is not only inefficient, costly, but often incomplete. YOLOv2 lacks the capture of small defects, YOLOv3 uses three scale feature maps for prediction, and enhances the detection of small cracks. This paper aims to propose a new application scenario for applying YOLOv3 to crack detection in floodgate dam surface and share its effects.

References

[1]

MWR, NBS, 2013, The first national census water resources announcement [M], China WaterPower Press.

[2]

Shucheng Wang, 2011, Reflections on some problems of dam construction[J], Journal of hydroelectricity.1--3.

[3]

Zongkun Li, Wei Ge, Juan Wang, et al. 2015(5), Research on risk standard and application of China reservoir dam, Journal of hydraulic, 567--573.

[4]

Cunguo Duan, 2011, Application research on fuzzy safety assessment of embankment rservoir dam[D], Shandong University.

[5]

Zhang L, Yang F, Zhang Y D, et al. 2016, Road crack detection using deep convolutional neural network[C], IEEE international conference on image processing (ICIP). IEEE, 3708--3712.

[6]

Gu C S, Zhao E F, Jin Y, et al. 2011, Singular value diagnosis in dam safety monitoring effect values[J]. Science China Technological Sciences, 54(5): 1169--1176.

[7]

Shi Y, Cui L, Qi Z, et al. 2016, Automatic road crack detection using random structured forests[J]. IEEE Transactions on Intelligent Transportation Systems, 17(12): 3434--3445.

Digital Library

[8]

Oliveira H, Correia P L. 2013, Automatic road crack detection and characterization[J]. IEEE Transactions on Intelligent Transportation Systems, 14(1): 155--168.

Digital Library

[9]

Paglinawan, Arnold C., et al. 2018, "Crack Detection Using Multiple Image Processing for Unmanned Aerial Monitoring of Concrete Structure." TENCON 2018-2018 IEEE Region 10 Conference. IEEE,

[10]

Bang, Seongdeok, et al. 2019, "Encoder--decoder network for pixel-level road crack detection in black-box images." Computer-Aided Civil and Infrastructure Engineering.

[11]

Jun Tan, 2018, Research on an improved yolov3 target recognition algorithm, Huazhong University of Science and Technology.

[12]

Tingting Chen, 2018, Research on airport identification based on high altitude reconnaissance image, Xi'an University of Architecture and Technology.

[13]

Saager Leif, Maiese Eric M, Bash Lori D, Meyer Tricia A, Minkowitz Harold, Groudine Scott, Philip Beverly K, Tanaka Pedro, Gan Tong Joo, Rodriguez-Blanco Yiliam, Soto Roy, Heisel Olaf. 2019, Incidence, risk factors, and consequences of residual neuromuscular block in the United States: The prospective, observational, multicenter RECITE-US study[J]. Journal of clinical anesthesia, 55.

[14]

Roger Pallarès-López, Felipe Costa Alvim, Míriam Febrer-Nafría, Luciano Luporini Menegaldo, Josep M. Font-Llagunes. 2019, Assessment of residual reduction procedures for high-speed tasks[J]. Gait & Posture, 73.

[15]

Jianguo Song, Yue Wu. 2019, Research review on aerial power line patrol robot[J/OL], Robot:1--4.

[16]

Zhiqiang Tan. 2019, On doubly robust estimation for logistic partially linear models[J]. Statistics and Probability Letters, 155.

[17]

Wenjing Cui, Yujing Wang, Shouqiang Kang, Jinbao Xie, Qingyan Wang, V.I.MIKULOVICH. Road lane detection based on improved YOLOv3 algorithm [J/OL]. Journal of automation:1--9.

[18]

Xinxiang Mao, Zhi Liu, Jingru Ren, Han Liu, Gongjun Zhang, Song Xue, Chuxiong Yuan, 2019, Surface defect detection system for continuous casting slab based on deep learning[J], Industrial control computer. 32(03):66--68.

[19]

Jinli Xue, Jianguo Dai, Qingzhan Zhao, Guoshun Zhang, Meina Cui, Nan Jiang. 2019, Weed detection in cotton fields based on low altitude uav image and YOLOv3 [J], Journal of shihezi university (natural science edition)37(01):21--27.

[20]

Yunchu Zhang, Zize Liang, Min Tan. 2004, Research review on aerial power line patrol robot [J]. Robot (9):467--473.

[21]

Gang Shen. 2016, Road Crack Detection Based on Video Image Processing[J]. ICSAI: 912--917.

[22]

Mingxia Shen, Meng Tai, CEDRIC OKinda, Longshen Liu, Jiawei Li, Yuwen Sun. 2009, Real-time target detection method for piglets based on deep convolutional neural network [J/OL]. Journal of agricultural machinery: 1--15.

[23]

Kun Zhao, Xiaoxi Ren. 2019, Small Aircraft Detection in Remote Sensing Images Based on YOLOv3[J]. IOP Conference Series: Materials Science and Engineering, 533(1).

[24]

Yanwei Zhao, Fen Zhu, Fangzhi Gui, Shedong Ren, Zhiwei Xie, Chen Xu. 2009, Improved k-means clustering algorithm based on extension distance [J/OL].Journal of intelligent systems:1--8.

[25]

Shuo Lv, Huan Cai, Rui Feng. 2019, YOLOv3 network based on improved loss function [J]. Application of computer system: 28(02):1--7.

Cited By

Araujo TTeixeira CFrancês C(2024)Enhancing geotechnical damage detection with deep learning: a convolutional neural network approachPeerJ Computer Science10.7717/peerj-cs.205210(e2052)Online publication date: 12-Aug-2024
https://doi.org/10.7717/peerj-cs.2052
Li LLiu RAli RChen BLin HLi YZhang H(2024)DFP-Net: A Crack Segmentation Method Based on a Feature Pyramid NetworkApplied Sciences10.3390/app1402065114:2(651)Online publication date: 12-Jan-2024
https://doi.org/10.3390/app14020651
Allah MToor IShams ASiddiqui O(2024)Application of Machine Learning and Deep Learning Techniques for Corrosion and Cracks Detection in Nuclear Power Plants: A ReviewArabian Journal for Science and Engineering10.1007/s13369-024-09388-6Online publication date: 12-Aug-2024
https://doi.org/10.1007/s13369-024-09388-6
Show More Cited By

Index Terms

Dam surface crack detection based on deep learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

Recommendations

The Effect of Different Filling Methods on Working Behavior of High Concrete Face Rock-Fill Dam
CSO '11: Proceedings of the 2011 Fourth International Joint Conference on Computational Sciences and Optimization

In order to research the effect of different filling methods on working behavior of high concrete face rock-fill dam (CFRD), the filling methods of CFRD in China were summarized in this paper. By three-dimensional nonlinear finite element method, ...
A challenging dam structural analysis: large-scale implicit thermo-mechanical coupled contact simulation on Tianhe-II

Due to huge bulk volume and extremely complex geometrical and geological features, it is forbiddingly difficult to perform a dam structural analysis with even moderate geometry fidelity in engineering practices. We present a high resolution of ...
Efficient real-time defect detection for spillway tunnel using deep learning
Abstract
A spillway tunnel eroded by high-speed water for a long time is prone to the rebar-exposed defects. Therefore, regular defect detection is very important for the safety of the hydropower station. The images of spillway tunnel are obtained by ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

RICAI '19: Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence

September 2019

803 pages

ISBN:9781450372985

DOI:10.1145/3366194

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

RICAI 2019

RICAI 2019: 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence

September 20 - 22, 2019

Shanghai, China

Acceptance Rates

RICAI '19 Paper Acceptance Rate 140 of 294 submissions, 48%;

Overall Acceptance Rate 140 of 294 submissions, 48%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
257
Total Downloads

Downloads (Last 12 months)27
Downloads (Last 6 weeks)1

Reflects downloads up to 18 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Araujo TTeixeira CFrancês C(2024)Enhancing geotechnical damage detection with deep learning: a convolutional neural network approachPeerJ Computer Science10.7717/peerj-cs.205210(e2052)Online publication date: 12-Aug-2024
https://doi.org/10.7717/peerj-cs.2052
Li LLiu RAli RChen BLin HLi YZhang H(2024)DFP-Net: A Crack Segmentation Method Based on a Feature Pyramid NetworkApplied Sciences10.3390/app1402065114:2(651)Online publication date: 12-Jan-2024
https://doi.org/10.3390/app14020651
Allah MToor IShams ASiddiqui O(2024)Application of Machine Learning and Deep Learning Techniques for Corrosion and Cracks Detection in Nuclear Power Plants: A ReviewArabian Journal for Science and Engineering10.1007/s13369-024-09388-6Online publication date: 12-Aug-2024
https://doi.org/10.1007/s13369-024-09388-6
Allah MShams AToor IIqbal N(2024)Comparative Study of Deep Learning and Machine Learning Techniques for Corrosion and Cracks Detection in Nuclear Power PlantsChallenges and Recent Advancements in Nuclear Energy Systems10.1007/978-3-031-64362-0_28(279-287)Online publication date: 21-Jul-2024
https://doi.org/10.1007/978-3-031-64362-0_28
Tao FPi YDeng MTang YYuan C(2023)Research on Intelligent Grading Evaluation of Water Conservancy Project Safety Risks Based on Deep LearningWater10.3390/w1508160715:8(1607)Online publication date: 20-Apr-2023
https://doi.org/10.3390/w15081607
Yu JXu YXing CZhou JPan P(2023)Pixel-Level Crack Detection and Quantification of Nuclear Containment with Deep LearningStructural Control and Health Monitoring10.1155/2023/99820802023(1-19)Online publication date: 10-Aug-2023
https://doi.org/10.1155/2023/9982080
Chu HChun P(2023)Fine‐grained crack segmentation for high‐resolution images via a multiscale cascaded networkComputer-Aided Civil and Infrastructure Engineering10.1111/mice.13111Online publication date: 17-Oct-2023
https://doi.org/10.1111/mice.13111
Duan CZhang JSun WYuan K(2023)A Crack Classification Method for Dam Image Based on Double-input Neural Network2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI)10.1109/ICETCI57876.2023.10176537(1742-1748)Online publication date: 26-May-2023
https://doi.org/10.1109/ICETCI57876.2023.10176537
Geng ZShi XHan YHu X(2023)Small Sample Containment Vessel Cracks Detection Algorithm Based the Latent Diffusion Model and Improved Faster-RCNN2023 7th Asian Conference on Artificial Intelligence Technology (ACAIT)10.1109/ACAIT60137.2023.10528449(1355-1359)Online publication date: 10-Nov-2023
https://doi.org/10.1109/ACAIT60137.2023.10528449
Xu GHan XZhang YWu C(2022)Dam Crack Image Detection Model on Feature Enhancement and Attention MechanismWater10.3390/w1501006415:1(64)Online publication date: 25-Dec-2022
https://doi.org/10.3390/w15010064
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents