Automatic Crack Detection and Measurement of Concrete Structure Using Convolutional Encoder-Decoder Network
The detection and measurement of crack at pixel level is a challenge to existing methods. To overcome this challenge, this paper proposes a convolutional encoder-decoder network (CedNet) to detect crack from image, and the maximum widths and orientations of cracks are measured using image post-proce...
Main Authors: | Shengyuan Li, Xuefeng Zhao |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9146278/ |
Similar Items
-
Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture
by: Zhun Fan, et al.
Published: (2020-07-01) -
Concrete Pavement Crack Detection Based on Dilated Convolution and Multi-features Fusion
by: QU Zhong, CHEN Wen
Published: (2022-03-01) -
Pedestrian Detection at Night in Infrared Images Using an Attention-Guided Encoder-Decoder Convolutional Neural Network
by: Yunfan Chen, et al.
Published: (2020-01-01) -
Constrained Image Splicing Detection and Localization With Attention-Aware Encoder-Decoder and Atrous Convolution
by: Yaqi Liu, et al.
Published: (2020-01-01) -
Deep Learning-Based Feature Silencing for Accurate Concrete Crack Detection
by: Umme Hafsa Billah, et al.
Published: (2020-08-01)