Detection and Length Measurement of Cracks Captured in Low Definitions Using Convolutional Neural Networks
Continuous efforts were made in detecting cracks in images. Varied CNN models were developed and tested for detecting or segmenting crack regions. However, most datasets used in previous works contained clearly distinctive crack images. No previous methods were validated on blurry cracks captured in...
Main Authors: | Jin-Young Kim, Man-Woo Park, Nhut Truong Huynh, Changsu Shim, Jong-Woong Park |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-04-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/23/8/3990 |
Similar Items
-
Crack Length Measurement Using Convolutional Neural Networks and Image Processing
by: Yingtao Yuan, et al.
Published: (2021-09-01) -
Predicting characteristics of cracks in concrete structure using convolutional neural network and image processing
by: Waqas Qayyum, et al.
Published: (2023-07-01) -
Crack-Length Estimation for Structural Health Monitoring Using the High-Frequency Resonances Excited by the Energy Release during Fatigue-Crack Growth
by: Roshan Joseph, et al.
Published: (2021-06-01) -
Efficient Dataset Collection for Concrete Crack Detection With Spatial-Adaptive Data Augmentation
by: Jong-Hyun Kim, et al.
Published: (2023-01-01) -
Experimental study on grouting reinforcement characteristics of limestone with different length cracks under fluid solid coupling
by: Juntao CHEN, et al.
Published: (2024-03-01)