A method of particleboard surface defect detection and recognition based on deep learning-Reference-Cited by-同舟云学术

A method of particleboard surface defect detection and recognition based on deep learning

Published:2024-03-09 Issue: Volume: Page:1-12
ISSN:1748-0272
Container-title:Wood Material Science & Engineering
language:en
Short-container-title:Wood Material Science & Engineering

Author:

Zhang Chengliang¹²^ORCID,Wang Chunling¹²,Zhao Liyuan³,Qu Xiaolong¹²,Gao Xujie¹²

Affiliation:

1. School of Information Science and Technology, Beijing Forestry University, Beijing, People’s Republic of China

2. National Forestry and Grassland Administration Engineering Research Center for Forestry-oriented Intelligent Information Processing, Beijing, People’s Republic of China

3. Research Institute of Wood Industry of Chinese Academy of Forestry, Beijing, People’s Republic of China

Funder

National Key RD Program of China

The Emergency Open Competition Project of National Forestry and Grassland Administration

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17480272.2024.2323579

Reference38 articles.

1. Acemoglu, D., and Restrepo, P., 2018. Artificial Intelligence, Automation, and Work (United States).

2. Akcay S. Atapour-Abarghouei A. and Breckon T.P. 2018. GANomaly: semi-supervised anomaly detection via adversarial training. Computer Vision – ACCV 2018. Cham: Springer.

3. Non-destructive evaluation of timber beams using vibration response;Budipriyanto A.;Nondestructive Testing and Evaluation,2008

4. Cristianini, N., and Shawe-Taylor, J., 2000. An introduction to support vector machines and other kernel-based learning methods. New York: Springer.

5. Advances and researches on non destructive testing: A review[J];Dwivedi S.K.;Materials Today: Proceedings,2018