Surface defect detection of steel strips based on classification priority YOLOv3-dense network
Author:
Affiliation:
1. Faculty of Engineering, Tokushima University, Tokushima, Japan
2. School of Mechanical Engineering, Nantong University, Nantong, People’s Republic of China
Funder
Research Clusters program of Tokushima University
Priority Academic Program Development of Jiangsu Higher Education Institutions
JJSPS KAKENHI
Publisher
Informa UK Limited
Subject
Materials Chemistry,Metals and Alloys,Mechanical Engineering,Mechanics of Materials
Link
https://www.tandfonline.com/doi/pdf/10.1080/03019233.2020.1816806
Reference55 articles.
1. Semi-supervised defect classification of steel surface based on multi-training and generative adversarial network
2. Online Surface Defect Identification of Cold Rolled Strips Based on Local Binary Pattern and Extreme Learning Machine
3. Review of vision-based steel surface inspection systems
4. Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection
5. Machine learning-based imaging system for surface defect inspection
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