A Deep-Convolutional-Neural-Network-Based Semi-Supervised Learning Method for Anomaly Crack Detection

A Deep-Convolutional-Neural-Network-Based Semi-Supervised Learning Method for Anomaly Crack Detection

Crack detection plays a pivotal role in structural health monitoring. Deep convolutional neural networks (DCNN) provide a way to achieve image classification efficiently and accurately due to their powerful image processing ability. In this paper, we propose a semi-supervised learning method based o...

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Bibliographic Details
Main Authors: Xingjun Gao, Chuansheng Huang, Shuai Teng, Gongfa Chen
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Applied Sciences
Subjects:
anomaly crack detection
deep convolutional neural network
semi-supervised learning
infrastructure cracks
neural network visualization
Online Access:https://www.mdpi.com/2076-3417/12/18/9244

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https://www.mdpi.com/2076-3417/12/18/9244

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