Surface Defect Detection for Mobile Phone Back Glass Based on Symmetric Convolutional Neural Network Deep Learning
Abstract
:1. Introduction
2. Imaging Capture System
3. Glass Surface Defect Dataset (GSDD)
4. Segmentation Model Architecture
5. Experiments
5.1. Training Setups
5.2. The Defection Detection Results
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Methods | Defect Type | Average Precision | Recall | Average Recall |
---|---|---|---|---|
Traditional method | Dent | 85.2% | 87.8% | 90.7% |
Scratch | 91.0% | |||
Discoloration | 92.9% | |||
Proposed deep learning method | Dent | 91.8% | 93.1% | 95.3% |
Scratch | 95.5% | |||
Discoloration | 97.0% |
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Jiang, J.; Cao, P.; Lu, Z.; Lou, W.; Yang, Y. Surface Defect Detection for Mobile Phone Back Glass Based on Symmetric Convolutional Neural Network Deep Learning. Appl. Sci. 2020, 10, 3621. https://doi.org/10.3390/app10103621
Jiang J, Cao P, Lu Z, Lou W, Yang Y. Surface Defect Detection for Mobile Phone Back Glass Based on Symmetric Convolutional Neural Network Deep Learning. Applied Sciences. 2020; 10(10):3621. https://doi.org/10.3390/app10103621
Chicago/Turabian StyleJiang, Jiabin, Pin Cao, Zichen Lu, Weimin Lou, and Yongying Yang. 2020. "Surface Defect Detection for Mobile Phone Back Glass Based on Symmetric Convolutional Neural Network Deep Learning" Applied Sciences 10, no. 10: 3621. https://doi.org/10.3390/app10103621
APA StyleJiang, J., Cao, P., Lu, Z., Lou, W., & Yang, Y. (2020). Surface Defect Detection for Mobile Phone Back Glass Based on Symmetric Convolutional Neural Network Deep Learning. Applied Sciences, 10(10), 3621. https://doi.org/10.3390/app10103621