.png){kind=icon}
返回
A Novel Data Augmentation Method for Improved Visual Crack Detection Using Generative Adversarial Networks
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3251988.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Condition monitoring and inspection are core activities for assessing and evaluating the health of critical infrastructure spanning from road networks to nuclear power stations. Defect detection on visual inspections of such assets is a field that enjoys increasing attention. However, data-based models are prone to a lack of available data depicting cracks of various modalities and present a great data imbalance. This paper introduces a novel data augmentation technique by deploying the CycleGan Generative Adversarial Network (GAN). The proposed model is deployed between different image datasets depicting cracks, with a nuclear application as the main industrial example. The aim of this network is to improve the segmentation accuracy on these datasets using deep convolutional neural networks. The proposed GAN generates realistic images that are challenging to segment and under-represented in the original datasets. Different deep networks are trained with the augmented datasets while introducing no labelling overhead. A comparison is drawn between the performance of the different neural networks on the original data and their augmented counterparts. Extensive experiments suggest that the proposed augmentation method results in superior crack detection in challenging cases across all datasets. This is reflected by the respective increase in the quantitative evaluation metrics.
Keyword:
Generative adversarial networks
Inspection
Image segmentation
Visualization
Task analysis
Data models
Data augmentation
Crack segmentation
generative adversarial networks (GANs)
nuclear inspections
data augmentation
image-to-image translation
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Generating ultrasonic images indistinguishable from real images using Generative Adversarial Networks.png)
ULTRASONICS
ULTRASONICSIF4.1
PREAI
分享收藏分享收藏Unsupervised Learning with Generative Adversarial Network for Automatic Tire Defect Detection from X-ray ImagesSENSORS
OAAI
SENSORSIF3.5
OAAI分享收藏Is SA shared or distributed in team work? An exploratory study in an intelligence analysis task
PREAI
分享收藏分享收藏
查看更多内容