Automated visual inspection of friction stir welds: a deep learning approach

2019 ◽  
Author(s):  
Roman Hartl ◽  
Johannes Landgraf ◽  
Julian Spahl ◽  
Andreas Bachmann ◽  
Michael F. Zaeh
Keyword(s):  
Deep Learning ◽  
Visual Inspection ◽  
Learning Approach ◽  
Automated Visual Inspection ◽  
Friction Stir

Automated visual inspection of fabric image using deep learning approach for defect detection

2021 ◽  
Author(s):  
Vyacheslav V. Voronin ◽  
Roman Sizyakin ◽  
Marina Zhdanova ◽  
Evgenii A. Semenishchev ◽  
Dmitry Bezuglov ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Visual Inspection ◽  
Learning Approach ◽  
Automated Visual Inspection

Automated visual inspection of target parts for train safety based on deep learning

2018 ◽  
Vol 12 (6) ◽  
pp. 550-555 ◽  
Author(s):  
Fuqiang Zhou ◽  
Ya Song ◽  
Liu Liu ◽  
Dongtian Zheng
Keyword(s):  
Deep Learning ◽  
Visual Inspection ◽  
Automated Visual Inspection ◽  
Train Safety

scholarly journals Automated Microfossil Identification and Segmentation Using a Deep Learning Approach

2019 ◽  
Author(s):  
L.E Carvalho ◽  
G. Fauth ◽  
S. Baecker Fauth ◽  
G. Krahl ◽  
A. C. Moreira ◽  
...  
Keyword(s):  
Deep Learning ◽  
Visual Inspection ◽  
Computational Analysis ◽  
Extraction Process ◽  
Oil Extraction ◽  
High Rate ◽  
Learning Approach ◽  
3D Images ◽  
Identification Process

AbstractThe applicability of computational analysis to paleontological images ranges from the study of the animals, plants and evolution of microorganisms to the simulation of the habitat of living beings of a given epoch. It also can be applied in several niches, such as oil exploration, where there are several factors to be analyzed in order to minimize the expenses related to the oil extraction process. One factor is the characterization of the environment to be explored. This analysis can occur in several ways: use of probes, extraction of samples for petrophysical components evaluation, the correlation with logs of other drilling wells and so on. In the samples extraction part the Computed Tomography (CT) is of importance because it preserves the sample and makes it available for several analyzes. Based on 3D images generated by CT, several analyzes and simulations can be performed and processes, currently performed manually and exhaustively, can be automated. In this work we propose and validate a method for fully automated microfossil identification and extraction. A pipeline is proposed that begins in the scanning process and ends in an identification process. For the identification a Deep Learning approach was developed, which resulted in a high rate of correct microfossil identification (98% of Intersection Over Union). The validation was performed both through an automated quantitative analysis based upon ground truths generated by specialists in the micropaleontology field and visual inspection by these specialists. We also present the first fully annotated MicroCT-acquired publicly available microfossils dataset.

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

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.

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