scholarly journals Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once

2021 ◽  
Vol 18 (4) ◽  
pp. 4411-4428
Author(s):  
Venkat Anil Adibhatla ◽  
◽  
Huan-Chuang Chih ◽  
Chi-Chang Hsu ◽  
Joseph Cheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Printed Circuit Boards ◽  
Circuit Boards ◽  
Printed Circuit

scholarly journals The Impact of a Number of Samples on Unsupervised Feature Extraction, Based on Deep Learning for Detection Defects in Printed Circuit Boards

2021 ◽  
Vol 14 (1) ◽  
pp. 8
Author(s):  
Ihar Volkau ◽  
Abdul Mujeeb ◽  
Wenting Dai ◽  
Marius Erdt ◽  
Alexei Sourin
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Printed Circuit Boards ◽  
Learning Model ◽  
Circuit Boards ◽  
Printed Circuit ◽  
Unsupervised Deep Learning ◽  
The Impact ◽  
Deep Learning Model ◽  
Unsupervised Feature Extraction

Deep learning provides new ways for defect detection in automatic optical inspections (AOI). However, the existing deep learning methods require thousands of images of defects to be used for training the algorithms. It limits the usability of these approaches in manufacturing, due to lack of images of defects before the actual manufacturing starts. In contrast, we propose to train a defect detection unsupervised deep learning model, using a much smaller number of images without defects. We propose an unsupervised deep learning model, based on transfer learning, that extracts typical semantic patterns from defect-free samples (one-class training). The model is built upon a pre-trained VGG16 model. It is further trained on custom datasets with different sizes of possible defects (printed circuit boards and soldered joints) using only small number of normal samples. We have found that the defect detection can be performed very well on a smooth background; however, in cases where the defect manifests as a change of texture, the detection can be less accurate. The proposed study uses deep learning self-supervised approach to identify if the sample under analysis contains any deviations (with types not defined in advance) from normal design. The method would improve the robustness of the AOI process to detect defects.

scholarly journals Defect Detection in Printed Circuit Boards Using You-Only-Look-Once Convolutional Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1547
Author(s):  
Venkat Anil Adibhatla ◽  
Huan-Chuang Chih ◽  
Chi-Chang Hsu ◽  
Joseph Cheng ◽  
Maysam F. Abbod ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Defect Detection ◽  
Printed Circuit Boards ◽  
Learning Algorithms ◽  
Detection Accuracy ◽  
Quality Inspection ◽  
Circuit Boards ◽  
Printed Circuit

In this study, a deep learning algorithm based on the you-only-look-once (YOLO) approach is proposed for the quality inspection of printed circuit boards (PCBs). The high accuracy and efficiency of deep learning algorithms has resulted in their increased adoption in every field. Similarly, accurate detection of defects in PCBs by using deep learning algorithms, such as convolutional neural networks (CNNs), has garnered considerable attention. In the proposed method, highly skilled quality inspection engineers first use an interface to record and label defective PCBs. The data are then used to train a YOLO/CNN model to detect defects in PCBs. In this study, 11,000 images and a network of 24 convolutional layers and 2 fully connected layers were used. The proposed model achieved a defect detection accuracy of 98.79% in PCBs with a batch size of 32.

scholarly journals TDD‐net: a tiny defect detection network for printed circuit boards

2019 ◽  
Vol 4 (2) ◽  
pp. 110-116 ◽  
Author(s):  
Runwei Ding ◽  
Linhui Dai ◽  
Guangpeng Li ◽  
Hong Liu
Keyword(s):  
Defect Detection ◽  
Printed Circuit Boards ◽  
Circuit Boards ◽  
Printed Circuit

scholarly journals Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2921
Author(s):  
Sumyung Gang ◽  
Ndayishimiye Fabrice ◽  
Daewon Chung ◽  
Joonjae Lee
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Printed Circuit Boards ◽  
Printed Circuit Board ◽  
Environmental Changes ◽  
Original Data ◽  
Circuit Board ◽  
Circuit Boards ◽  
Printed Circuit ◽  
Proposed Model

As the size of components mounted on printed circuit boards (PCBs) decreases, defect detection becomes more important. The first step in an inspection involves recognizing and inspecting characters printed on parts attached to the PCB. In addition, since industrial fields that produce PCBs can change very rapidly, the style of the collected data may vary between collection sites and collection periods. Therefore, flexible learning data that can respond to all fields and time periods are needed. In this paper, large amounts of character data on PCB components were obtained and analyzed in depth. In addition, we proposed a method of recognizing characters by constructing a dataset that was robust with various fonts and environmental changes using a large amount of data. Moreover, a coreset capable of evaluating an effective deep learning model and a base set using n-pick sampling capable of responding to a continuously increasing dataset were proposed. Existing original data and the EfficientNet B0 model showed an accuracy of 97.741%. However, the accuracy of our proposed model was increased to 98.274% for the coreset of 8000 images per class. In particular, the accuracy was 98.921% for the base set with only 1900 images per class.

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