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SG11202101353TA - A method and apparatus for training a neural network to identify cracks - Google Patents

A method and apparatus for training a neural network to identify cracks

Info

Publication number
SG11202101353TA
SG11202101353TA SG11202101353TA SG11202101353TA SG11202101353TA SG 11202101353T A SG11202101353T A SG 11202101353TA SG 11202101353T A SG11202101353T A SG 11202101353TA SG 11202101353T A SG11202101353T A SG 11202101353TA SG 11202101353T A SG11202101353T A SG 11202101353TA
Authority
SG
Singapore
Prior art keywords
training
neural network
identify cracks
cracks
identify
Prior art date
Application number
SG11202101353TA
Inventor
Fen Fang
Liyuan Li
Joo Hwee Lim
Original Assignee
Agency Science Tech & Res
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Agency Science Tech & Res filed Critical Agency Science Tech & Res
Publication of SG11202101353TA publication Critical patent/SG11202101353TA/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30132—Masonry; Concrete
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30184—Infrastructure

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Probability & Statistics with Applications (AREA)
  • Mathematical Analysis (AREA)
  • Computational Mathematics (AREA)
  • Algebra (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Quality & Reliability (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Image Analysis (AREA)
SG11202101353TA 2018-08-31 2019-08-30 A method and apparatus for training a neural network to identify cracks SG11202101353TA (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
SG10201807490P 2018-08-31
PCT/SG2019/050433 WO2020046213A1 (en) 2018-08-31 2019-08-30 A method and apparatus for training a neural network to identify cracks

Publications (1)

Publication Number Publication Date
SG11202101353TA true SG11202101353TA (en) 2021-03-30

Family

ID=69645838

Family Applications (1)

Application Number Title Priority Date Filing Date
SG11202101353TA SG11202101353TA (en) 2018-08-31 2019-08-30 A method and apparatus for training a neural network to identify cracks

Country Status (2)

Country Link
SG (1) SG11202101353TA (en)
WO (1) WO2020046213A1 (en)

Families Citing this family (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111612747B (en) * 2020-04-30 2023-10-20 湖北煌朝智能自动化装备有限公司 Rapid detection method and detection system for product surface cracks
CN111563888A (en) * 2020-05-06 2020-08-21 清华大学 Quantitative crack growth monitoring method
CN112419244B (en) * 2020-11-11 2022-11-01 浙江大学 Concrete crack segmentation method and device
CN112634195B (en) * 2020-11-23 2024-10-01 清华大学 Concrete structure crack prediction method, device and system
CN112465776B (en) * 2020-11-26 2023-10-31 常州信息职业技术学院 Crack intelligent detection method based on wind turbine surface blurred image
US11544919B2 (en) 2020-12-09 2023-01-03 Precisionhawk, Inc. Drone inspection of an undifferentiated surface using a reference image
CN112581441B (en) * 2020-12-10 2024-07-05 北京工业大学 Pavement crack detection method and device, electronic equipment and storage medium
CN112700423B (en) * 2021-01-06 2023-05-05 中国民航科学技术研究院 Automatic detection method and system for fuselage surface damage defects based on deep learning
CN113011397B (en) * 2021-04-27 2024-03-29 北京工商大学 Multi-factor cyanobacterial bloom prediction method based on remote sensing image 4D-Fractalnet
CN113627475A (en) * 2021-07-07 2021-11-09 厦门市美亚柏科信息股份有限公司 Method and device for carrying out uncertainty estimation on sample
US20230080178A1 (en) * 2021-09-02 2023-03-16 Northeastern University Automated assessment of cracks using lidar and camera data
CN113838013A (en) * 2021-09-13 2021-12-24 中国民航大学 Blade crack real-time detection method and device in aero-engine operation and maintenance based on YOLOv5
CN114418937B (en) * 2021-12-06 2022-10-14 北京邮电大学 Pavement crack detection method and related equipment
CN115184372B (en) * 2022-07-13 2023-04-18 水利部交通运输部国家能源局南京水利科学研究院 Intelligent detection device and method for micro-crack fluorescence permeation of inaccessible part of concrete structure
CN115423829B (en) * 2022-07-29 2024-03-01 江苏省水利科学研究院 Method and system for rapidly extracting water body of single-band remote sensing image
CN116777865B (en) * 2023-06-16 2024-09-06 广州大学 A method, system, device and storage medium for identifying underwater cracks
CN116895008A (en) * 2023-07-17 2023-10-17 中国长江三峡集团有限公司 Crack identification model determination and crack identification method, device, equipment and medium
CN117540626B (en) * 2023-10-30 2024-05-14 南通大学 Fixed wing unmanned aerial vehicle situation prediction method based on Bayesian neural network
CN117495147B (en) * 2023-12-22 2024-03-22 中国石油大学(华东) An intelligent prediction method for fracture network expansion considering differences in fracturing processes between sections
CN117875549B (en) * 2023-12-29 2024-11-05 昆明理工大学 Building heritage protection evaluation system and method based on image recognition
CN118036476B (en) * 2024-04-11 2024-07-02 合肥工业大学 Precast concrete crack detection model, method, system and readable medium
CN118781431B (en) * 2024-07-19 2025-02-11 苏州旗开得电子科技有限公司 Classification method of SMT 3D image data based on deep learning framework
CN119693302A (en) * 2024-11-15 2025-03-25 常州百利锂电智慧工厂有限公司 A method for detecting sagger defects
CN119540240B (en) * 2025-01-22 2025-04-25 浙江华诚工程管理有限公司 A bridge crack detection method based on image recognition

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105787486B (en) * 2015-12-11 2019-04-09 昆明理工大学 A method for crack detection of steel beams based on image processing
CA3056498A1 (en) * 2017-03-14 2018-09-20 University Of Manitoba Structure defect detection using machine learning algorithms
CN108198173B (en) * 2017-12-28 2019-11-26 石家庄铁道大学 A kind of online test method, device and the terminal device in distress in concrete region

Also Published As

Publication number Publication date
WO2020046213A1 (en) 2020-03-05

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