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 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 Deep Learning Convolutional Neural Networks for Pharmaceutical Tablet Defect Detection

2020 ◽  
Vol 26 (S2) ◽  
pp. 1606-1609
Author(s):  
Xiangyu Ma ◽  
Nada Kittikunakorn ◽  
Bradley Sorman ◽  
Hanmi Xi ◽  
Antong Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Defect Detection ◽  
Pharmaceutical Tablet

scholarly journals Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Mundher Mohammed Taresh ◽  
Ningbo Zhu ◽  
Talal Ahmed Ali Ali ◽  
Asaad Shakir Hameed ◽  
Modhi Lafta Mutar
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Detection Accuracy ◽  
X Rays ◽  
X Ray ◽  
Specificity And Sensitivity ◽  
X Ray Imaging ◽  
Novel Coronavirus

The novel coronavirus disease 2019 (COVID-19) is a contagious disease that has caused thousands of deaths and infected millions worldwide. Thus, various technologies that allow for the fast detection of COVID-19 infections with high accuracy can offer healthcare professionals much-needed help. This study is aimed at evaluating the effectiveness of the state-of-the-art pretrained Convolutional Neural Networks (CNNs) on the automatic diagnosis of COVID-19 from chest X-rays (CXRs). The dataset used in the experiments consists of 1200 CXR images from individuals with COVID-19, 1345 CXR images from individuals with viral pneumonia, and 1341 CXR images from healthy individuals. In this paper, the effectiveness of artificial intelligence (AI) in the rapid and precise identification of COVID-19 from CXR images has been explored based on different pretrained deep learning algorithms and fine-tuned to maximise detection accuracy to identify the best algorithms. The results showed that deep learning with X-ray imaging is useful in collecting critical biological markers associated with COVID-19 infections. VGG16 and MobileNet obtained the highest accuracy of 98.28%. However, VGG16 outperformed all other models in COVID-19 detection with an accuracy, F1 score, precision, specificity, and sensitivity of 98.72%, 97.59%, 96.43%, 98.70%, and 98.78%, respectively. The outstanding performance of these pretrained models can significantly improve the speed and accuracy of COVID-19 diagnosis. However, a larger dataset of COVID-19 X-ray images is required for a more accurate and reliable identification of COVID-19 infections when using deep transfer learning. This would be extremely beneficial in this pandemic when the disease burden and the need for preventive measures are in conflict with the currently available resources.

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