An efficient lightweight convolutional neural network for industrial surface defect detection-Reference-Cited by-同舟云学术

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An efficient lightweight convolutional neural network for industrial surface defect detection

Published:2023-03-01 Issue:9 Volume:56 Page:10651-10677
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Zhang Dehua,Hao Xinyuan,Wang Dechen,Qin Chunbin,Zhao Bo^ORCID,Liang Linlin,Liu Wei

Funder

Key Scientific Research Project of Colleges and Universities in Henan Province

National Natural Science Foundation of China

Foundation of Excellent Young-Backbone Teacher of Colleges and Universities in Henan Province

Postgraduate Cultivating Innovation and Quality Improvement Action Plan of Henan University

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-023-10438-y.pdf

Reference61 articles.

1. Aiger D, Talbot H (2010) The phase only transform for unsupervised surface defect detection. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). pp 295–302. https://doi.org/10.1109/CVPR.2010.5540198

2. Alex S, Dhanaraj KJ, Deepthi PP (2022) Private and energy-efficient decision tree-based disease detection for resource-constrained medical users in mobile healthcare network. IEEE Access 10:17098–17112. https://doi.org/10.1109/ACCESS.2022.3149771

3. Amir NIM, Dziyauddin RA, Mohamed N, et al (2022) Real-time threshold-based fall detection system using wearable iot. In: 2022 4th international conference on smart sensors and application (ICSSA). pp 173–178. https://doi.org/10.1109/ICSSA54161.2022.9870974

4. Bochkovskiy A, Wang CY, Liao H (2020) YOLOv4: optimal speed and accuracy of object detection. Preprint at http://arxiv.org/abs/2004.10934, https://doi.org/10.48550/arXiv.2004.10934

5. Cha YJ, Choi W, Büyüköztürk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput-Aided Civil Infrastruct Eng 32(5):361–378. https://doi.org/10.1111/mice.12263

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