Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed
Abstract
:1. Introduction
2. Recent Studies
2.1. Research Progress of Weld Defect Detection
2.2. Research Progress of Weld Defect Image Recognition
2.3. Summary of Related Studies
3. Materials and Methods
3.1. Analysis of Steel Plate’s Surface Defects
3.2. Detection Technologies for Steel Plate’s Surface Defects
3.2.1. Traditional Detection Technology
3.2.2. Deep Learning Detection Technology
3.3. Image Preprocessing
3.3.1. Image Denoising
3.3.2. Image Enhancement
3.4. Deep Learning Neural Networks
3.4.1. Convolution Neural Networks
3.4.2. Transfer Learning
3.5. Deep Learning-Based Image Defect Recognition Model
3.6. Experimental Data and Performance Evaluation
3.6.1. Experimental Environment and Data
3.6.2. Image Partition and Labeling
3.6.3. Model Performance Evaluation
4. Results and Discussion
4.1. Image Processing Results of Weld Defects
4.2. Performance Comparison of Different Training Models
4.3. Comparative Experiment of Industrial Weld Defect Images
4.4. Results of Other Performance Indicators
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Software and Hardware | Specific Configuration Information |
---|---|
Central Processing Unit | Inter(R)-Core (TM) I5-2400 |
RAM | DDR4 16 G |
CPU Hertz | 3.10 GHz |
Operating system | Ubuntu 16.04 |
Programming environment | Python 3.5 |
Type of Defect | Gas Pores (GP) | Flawless (FL) | Lack of Fusion (LOF) | Lack of Penetration (LOP) | Cracks (CK) |
---|---|---|---|---|---|
Training set | 1200 | 1040 | 920 | 680 | 320 |
Validation set | 150 | 130 | 115 | 85 | 40 |
Test set | 150 | 130 | 115 | 85 | 40 |
Label | 1 | 2 | 3 | 4 | 5 |
Type of Defect | GP | FL | LOF | LOP | CK | ACC/% |
---|---|---|---|---|---|---|
CNN-Train_All | 20 | 20 | 20 | 19 | 18 | 97 |
CNN-Frozen_C1C2 | 20 | 20 | 18 | 18 | 18 | 94 |
CNN-Frozen_C1C2C3 | 20 | 19 | 18 | 17 | 17 | 91 |
CNN-Frozen_C1C2C3C4 | 19 | 19 | 17 | 17 | 16 | 86 |
ACC/% | 98.75 | 97.50 | 91.25 | 88.75 | 86.25 | - |
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Deng, H.; Cheng, Y.; Feng, Y.; Xiang, J. Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed. Symmetry 2021, 13, 1731. https://doi.org/10.3390/sym13091731
Deng H, Cheng Y, Feng Y, Xiang J. Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed. Symmetry. 2021; 13(9):1731. https://doi.org/10.3390/sym13091731
Chicago/Turabian StyleDeng, Honggui, Yu Cheng, Yuxin Feng, and Junjiang Xiang. 2021. "Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed" Symmetry 13, no. 9: 1731. https://doi.org/10.3390/sym13091731
APA StyleDeng, H., Cheng, Y., Feng, Y., & Xiang, J. (2021). Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed. Symmetry, 13(9), 1731. https://doi.org/10.3390/sym13091731