Machine Vision Schemes towards Detecting and Estimating The State Of Corrosion

2011 ◽  
pp. 146-165
Author(s):  
P. Kapsalas ◽  
M. Zervakis ◽  
P. Maravelaki-Kalaitzaki ◽  
E.T. Delegou ◽  
A. Moropoulou
Keyword(s):  
Machine Vision ◽  
Corrosion Damage ◽  
Systematic Analysis ◽  
Distribution Shape ◽  
Cultural Heritage Objects ◽  
Detection Schemes ◽  
Non Destructive ◽  
Statistical Metrics ◽  
Stone Degradation ◽  
Detection And Quantification

The systematic analysis of corrosion damage on cultural heritage objects is an aspect of multidisciplinary interest. The application of computer-aided approaches in corrosion control has recently become a challenging issue. However, the majority of researches attain to estimate the decay presence by evaluating colour and texture alterations. This work is geared towards investigating non-destructive detection and quantification of stone degradation by using machine vision schemes. The contribution of the current work is 4-fold. Thus, (1) several detection schemes were developed; each handling in a different way the background in-homogeneity (2) Numerous statistical metrics were introduced to quantify corrosion damage. These metrics mainly consider the decay areas size, spatial distribution, shape and darkness. (3) The potential of several monitoring modalities in determining corrosion attributes is studied, and (4) the corroded areas’ shape features are considered in association with the cleaning and structural state that they represent.

scholarly journals The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Supakorn Harnsoongnoen ◽  
Nuananong Jaroensuk
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Low Cost ◽  
Measuring System ◽  
Coefficient Of Determination ◽  
Water Displacement ◽  
Non Invasive ◽  
Future Potential ◽  
Non Destructive ◽  
Egg Freshness

AbstractThe water displacement and flotation are two of the most accurate and rapid methods for grading and assessing freshness of agricultural products based on density determination. However, these techniques are still not suitable for use in agricultural inspections of products such as eggs that absorb water which can be considered intrusive or destructive and can affect the result of measurements. Here we present a novel proposal for a method of non-destructive, non-invasive, low cost, simple and real—time monitoring of the grading and freshness assessment of eggs based on density detection using machine vision and a weighing sensor. This is the first proposal that divides egg freshness into intervals through density measurements. The machine vision system was developed for the measurement of external physical characteristics (length and breadth) of eggs for evaluating their volume. The weighing system was developed for the measurement of the weight of the egg. Egg weight and volume were used to calculate density for grading and egg freshness assessment. The proposed system could measure the weight, volume and density with an accuracy of 99.88%, 98.26% and 99.02%, respectively. The results showed that the weight and freshness of eggs stored at room temperature decreased with storage time. The relationship between density and percentage of freshness was linear for the all sizes of eggs, the coefficient of determination (R2) of 0.9982, 0.9999, 0.9996, 0.9996 and 0.9994 for classified egg size classified 0, 1, 2, 3 and 4, respectively. This study shows that egg freshness can be determined through density without using water to test for water displacement or egg flotation which has future potential as a measuring system important for the poultry industry.

Textile Museum Collections. SIM Method Validation for the Assessment of Pesticides

2021 ◽  
Vol 58 (3) ◽  
pp. 1-10
Author(s):  
Elena-Cornelia Mitran ◽  
Irina-Mariana Sandulache ◽  
Cristina-Mihaela Lite ◽  
Lucian Gabriel Radu
Keyword(s):  
Analytical Techniques ◽  
High Sensitivity ◽  
Pest Infestation ◽  
Accuracy And Precision ◽  
Cis And Trans Isomers ◽  
Quantitative Results ◽  
Cis And Trans ◽  
High Degree ◽  
Non Destructive ◽  
Detection And Quantification

In time the environmental conditions could damage textiles (materials/ artifacts) causing the need to develop better non-destructive or at least micro-destructive analysis techniques of the samples. There are ethnographic textile artifacts that were treated in the past with various pesticides, that have not been mentioned in any document. These are often re-treated with chemicals by museum staff as a method of preventing pest infestation. Due to the progressive use of many pesticides, this paper was focused on the detection and quantification of three pesticides: malathion, methoxychlor, and permethrin (cis- and trans- isomers). Gas chromatography is one of the most widely used analytical techniques for characterizing volatile organic compounds and therefore was the analytical method of choice for the present study. Because these analytes are found at trace levels, the detection and quantification limits of analytes are very small and it is necessary to optimize and validate a SIM method - that allows the mass spectrometer to detect specific compounds with high sensitivity. In SIM mode, the instrument is set to collect data at selected masses of interest, thus increasing the accuracy and precision of the quantitative results. The present paper is aimed to develop this type of method with specificity and selectivity, high precision (expressed in terms of repeatability and intermediate accuracy), accuracy, suitable working range and linearity, and high degree of series� homogenity.

scholarly journals A framework for corrosion assessment in metallic structures, from data analysis to risk based inspection

2021 ◽  
Vol 23 (1) ◽  
pp. 11-20
Author(s):  
Xiaofei Cui ◽  
Xiaoxia Liang ◽  
Ujjwal Bharadwaj
Keyword(s):  
Corrosion Damage ◽  
Quantitative Information ◽  
Assessment Process ◽  
Steel Bridge ◽  
Destructive Testing ◽  
Metallic Structures ◽  
Wide Range ◽  
Corrosion Assessment ◽  
Metallic Corrosion ◽  
Non Destructive

Metallic corrosion is a big challenge affecting many sectors in a nation’s economy. Necessary corrosion prevention actions have to be taken in order to maintain the integrity of engineering assets susceptible to corrosion. This paper proposes a holistic framework to support the management of corrosion in metallic structures. It is a fully automation corrosion assessment process, with risk updated by Bayesian theory. Through analyzing the thickness data measured by non-destructive testing (NDT) techniques, the influence of corrosion on the component can be estimated using statistical methods, which will enable users to make decisions on maintenance based on quantitative information. A case study using corrosion data from a steel bridge is included to demonstrate the proposed framework. It improved the conventional corrosion analysis method by the proposed statistical approach using representative thickness data, which aims to take full use of the remaining life. This model can be adapted to a wide range of metallic structure suffering from corrosion damage.

Particle Induced X-ray Emission (PIXE) and Its Applications for Ceramic Analysis

2016 ◽  
pp. 381-398
Author(s):  
Marcia Rizzutto ◽  
Manfredo Tabacniks
Keyword(s):  
Cultural Heritage ◽  
Systematic Research ◽  
Ceramic Analysis ◽  
Pixe Analysis ◽  
Preventive Conservation ◽  
Scientific Methods ◽  
History Of ◽  
Cultural Heritage Objects ◽  
History Of Migration ◽  
Non Destructive

Systematic research into art and cultural heritage objects in museum collections are growing daily across the world. They are generally undertaken in partnership with archaeologists, curators, historians, conservators, and restorers. The use of scientific methods to answer specific questions about objects produced by different societies reveals the materials and technologies used in the past and gives us a better understanding of the history of migration processes, cultural characteristics, and thereby more grounded parameters for the preservation and conservation of cultural heritage. The use of non-destructive methods, such as the PIXE analysis, is very suitable in such studies because damage or alteration is avoided and the integrity of the object maintained. Such techniques gave historians and curators at the Archaeological and Ethnology Museum in São Paulo new understanding of the Chimu collection of ceramics as well as of the technical process of preventive conservation.

Non-destructive freeze damage detection in oranges using machine vision and ultraviolet fluorescence

2008 ◽  
Vol 48 (3) ◽  
pp. 341-346 ◽  
Author(s):  
D.C. Slaughter ◽  
D.M. Obenland ◽  
J.F. Thompson ◽  
M.L. Arpaia ◽  
D.A. Margosan
Keyword(s):  
Machine Vision ◽  
Damage Detection ◽  
Freeze Damage ◽  
Non Destructive

scholarly journals Automatic Non-destructive Quality Inspection System for Oil Palm Fruits

2014 ◽  
Vol 28 (3) ◽  
pp. 319-329 ◽  
Author(s):  
Muhammad Makky ◽  
Peeyush Soni ◽  
Vilas M. Salokhe
Keyword(s):  
Fatty Acid ◽  
Free Fatty Acid ◽  
Machine Vision ◽  
Oil Palm ◽  
Oil Content ◽  
Linear Regression Analysis ◽  
Field Tests ◽  
Free Fatty Acid Level ◽  
Inspection System ◽  
Non Destructive

Abstract In this research a non-destructive, rapid and cost effective examination machine for the estimation of the ripeness fraction, oil content and free fatty acid level in oil palm fresh fruits bunch was developed. The automatic machine-vision based inspection system provided consistency, rapid estimation and acceptable accuracy results in non-destructive manner. Fresh fruits bunch samples from Tenera cultivar (7 to 20 years trees) were taken from Cimulang plantation, Bogor, Indonesia. Two statistical analysis methods were used: a forward stepwise multiple linear regression analysis and a multilayer-perceptron artificial neural network analysis. The best prediction of ripeness and oil content models were obtained using the latter method, while the best free fatty acid prediction model was developed by the first method. The models were then employed in the machine-vision inspection systems of the machine. The system best prediction accuracy of ripeness, oil content and free fatty acid models was 93.5, 96.41, and 89.32%, with standard error of prediction being 0.065, 0.044 and 0.068, respectively. The system was tested through a series of field tests, and successfully examined more than 12 t of fruits bunch per hour, without causing damage.

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