Automated defect inspection system for metal surfaces based on deep learning and data augmentation

2020 ◽  
Vol 55 ◽  
pp. 317-324 ◽  
Author(s):  
Jong Pil Yun ◽  
Woosang Crino Shin ◽  
Gyogwon Koo ◽  
Min Su Kim ◽  
Chungki Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Metal Surfaces ◽  
Data Augmentation ◽  
Inspection System ◽  
Defect Inspection

scholarly journals CNN-Based Defect Inspection for Injection Molding Using Edge Computing and Industrial IoT Systems

2021 ◽  
Vol 11 (14) ◽  
pp. 6378
Author(s):  
Hyeonjong Ha ◽  
Jongpil Jeong
Keyword(s):  
Injection Molding ◽  
Data Augmentation ◽  
Smart Factory ◽  
Inspection System ◽  
Defect Inspection ◽  
Cycle Times ◽  
Injection Process ◽  
Imbalance Problem ◽  
Industrial Iot ◽  
Using Data

Currently, the development of automated quality inspection is drawing attention as a major component of the smart factory. However, injection molding processes have not received much attention in this area of research because of product diversity, difficulty in obtaining uniform quality product images, and short cycle times. In this study, we proposed a defect inspection system for injection molding in edge intelligence. Using data augmentation, we solved the data shortage and imbalance problem of small and medium-sized enterprises (SMEs), introduced the actual smart factory method of the injection process, and measured the performance of the developed artificial intelligence model. The accuracy of the proposed model was more than 90%, proving that the system can be applied in the field.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Methodology to Correlate Defect Reduction Systems to Electrical Test Data via Artificially Manufactured Defects

2002 ◽  
Author(s):  
Mike Santana ◽  
Alfredo V. Herrera
Keyword(s):  
Test Data ◽  
Learning Opportunities ◽  
Inspection System ◽  
Navigation Systems ◽  
Defect Inspection ◽  
Defect Reduction ◽  
Electrical Test ◽  
Important System

Abstract This paper describes a methodology for correlating physical defect inspection/navigation systems with electrical bitmap data through the fabrication of artificial defects via reticle alterations or circuit modifications using an inline FIB. The methodology chosen consisted of altering decommissioned reticles to create defects resulting in both open and shorted circuits within areas of an AMD microprocessor cache. The reticles were subsequently scanned using a KLA SL300HR StarLight inspection system to confirm their location, while wafers processed on these reticles were scanned at several layers using standard inline metrology. Finally, the wafers were electrically tested, bitmapped, and physically deprocessed. All defect data was then analyzed and cross-correlated between each system, uncovering some important system deficiencies and learning opportunities. Data and images are included to support the significance and effectiveness of such a methodology.

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

scholarly journals Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu
Keyword(s):  
Deep Learning ◽  
Integrated Pest Management ◽  
Pest Management ◽  
Real Time ◽  
Oilseed Rape ◽  
Data Augmentation ◽  
Low Cost ◽  
Response Speed ◽  
Original Model ◽  
Real Time System

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).

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