Multi-stage generative adversarial networks for generating pavement crack images
The application of machine learning techniques in pavement health monitoring based on computer vision has greatly improved the accuracy and efficiency in the detection of pavement distress levels and categories. However, a persistent challenge in this field is the issue of sample imbalance, primaril...
Saved in:
Main Authors: | Han, Chengjia, Ma, Tao, Huyan, Ju, Tong, Zheng, Yang, Handuo, Yang, Yaowen |
---|---|
Other Authors: | School of Civil and Environmental Engineering |
Format: | Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/180178 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
CrackDiffusion: a two-stage semantic segmentation framework for pavement crack combining unsupervised and supervised processes
by: Han, Chengjia, et al.
Published: (2024) -
Be a cartoonist : editing anime images using generative adversarial network
by: Koh, Tong Liang
Published: (2022) -
Augmenting image data using generative adversarial networks (GAN)
by: Liu, Xinchi
Published: (2024) -
Automatic pixel-level pavement crack detection using information of multi-scale neighborhoods
by: Ai, Dihao, et al.
Published: (2018) -
A review on generative adversarial networks: algorithms, theory, and applications
by: Gui, Jie, et al.
Published: (2022)