Application of Deep Learning in Surface Defect Inspection of Ring Magnets

2019 ◽  
pp. 115-122
Author(s):  
Xu Wang ◽  
Pan Cheng
Keyword(s):  
Deep Learning ◽  
Surface Defect ◽  
Defect Inspection

scholarly journals A deep learning-based surface defect inspection system using multi-scale and channel-compressed features

2020 ◽  
pp. 1-1
Author(s):  
Jiangxin Yang ◽  
Guizhong Fu ◽  
Wenbin Zhu ◽  
Yanlong Cao ◽  
Yanpeng Cao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Surface Defect ◽  
Inspection System ◽  
Defect Inspection ◽  
Multi Scale

scholarly journals A Rapid and Accurate Technique with Updating Strategy for Surface Defect Inspection of Pipelines

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Y. Da ◽  
B. Wang ◽  
D. Liu ◽  
Z. Qian
Keyword(s):  
Surface Defect ◽  
Defect Inspection

scholarly journals Efficient Cell Segmentation from Electroluminescent Images of Single-Crystalline Silicon Photovoltaic Modules and Cell-Based Defect Identification Using Deep Learning with Pseudo-Colorization

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4292
Author(s):  
Horng-Horng Lin ◽  
Harshad Kumar Dandage ◽  
Keh-Moh Lin ◽  
You-Teh Lin ◽  
Yeou-Jiunn Chen
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Crystalline Silicon ◽  
Cell Segmentation ◽  
Solar Pv ◽  
Single Crystalline Silicon ◽  
Defect Inspection ◽  
Defect Identification ◽  
Pv Modules ◽  
A Cell

Solar cells may possess defects during the manufacturing process in photovoltaic (PV) industries. To precisely evaluate the effectiveness of solar PV modules, manufacturing defects are required to be identified. Conventional defect inspection in industries mainly depends on manual defect inspection by highly skilled inspectors, which may still give inconsistent, subjective identification results. In order to automatize the visual defect inspection process, an automatic cell segmentation technique and a convolutional neural network (CNN)-based defect detection system with pseudo-colorization of defects is designed in this paper. High-resolution Electroluminescence (EL) images of single-crystalline silicon (sc-Si) solar PV modules are used in our study for the detection of defects and their quality inspection. Firstly, an automatic cell segmentation methodology is developed to extract cells from an EL image. Secondly, defect detection can be actualized by CNN-based defect detector and can be visualized with pseudo-colors. We used contour tracing to accurately localize the panel region and a probabilistic Hough transform to identify gridlines and busbars on the extracted panel region for cell segmentation. A cell-based defect identification system was developed using state-of-the-art deep learning in CNNs. The detected defects are imposed with pseudo-colors for enhancing defect visualization using K-means clustering. Our automatic cell segmentation methodology can segment cells from an EL image in about 2.71 s. The average segmentation errors along the x-direction and y-direction are only 1.6 pixels and 1.4 pixels, respectively. The defect detection approach on segmented cells achieves 99.8% accuracy. Along with defect detection, the defect regions on a cell are furnished with pseudo-colors to enhance the visualization.

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