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Tytuł:
Application of Artificial Neural Networks for Defect Detection in Ceramic Materials
Autorzy:
Akinci, T. C.
Nogay, H. S.
Yilmaz, O.
Powiązania:
https://bibliotekanauki.pl/articles/176701.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
impulse noise
artificial neural network
ANN
defect detection
ceramic materials
Opis:
In this study, an artificial neural network application was performed to tell if 18 plates of the same material in different shapes and sizes were cracked or not. The cracks in the cracked plates were of different depth and sizes and were non-identical deformations. This ANN model was developed to detect whether the plates under test are cracked or not, when four plates have been selected randomly from among a total of 18 ones. The ANN model used in the study is a model uniquely tailored for this study, but it can be applied to all systems by changing the weight values and without changing the architecture of the model. The developed model was tested using experimental data conducted with 18 plates and the results obtained mainly correspond to this particular case. But the algorithm can be easily generalized for an arbitrary number of items.
Źródło:
Archives of Acoustics; 2012, 37, 3; 279-286
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Defect Detection in Ceramic Materials Based on Time-Frequency Analysis by Using the Method of Impulse Noise
Autorzy:
Akinci, T. C.
Powiązania:
https://bibliotekanauki.pl/articles/177471.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
impulse noise
time-frequency analysis
defect detection in ceramic materials
Opis:
In this study, it was achieved by using the method of impulse noise to detect internal or surface cracks that can occur in the production of ceramic plates. Ceramic materials are often used in the industry, especially as kitchenware and in areas such as the construction sector. Many different methods are used in the quality assurance processes of ceramic materials. In this study, the impact noise method was examined. This method is a test technique that was not used in applications. The method is presented as an examination technique based on whether there is a deformation on the material according to the sound coming from it as a result of a plastic bit hammer impact on the ceramic material. The application of the study was performed on plates made of ceramic materials. Here, it was made with the same type of model plates manufactured from the same material. The noise that would occur as a result of the impact applied on a point determined on the materials to be tested has been examined by the method of time-frequency analysis. The method applied gives pretty good results for distinguishing ceramic plates in good condition from those which are cracked.
Źródło:
Archives of Acoustics; 2011, 36, 1; 77-85
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Industrial Application of Deep Neural Network for Aluminum Casting Defect Detection in Case of Unbalanced Dataset
Autorzy:
Awtoniuk, Michał
Majerek, Dariusz
Myziak, Artur
Gajda, Cyprian
Powiązania:
https://bibliotekanauki.pl/articles/2204946.pdf
Data publikacji:
2022
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
machine learning
deep neural network
classification
casting defect
casting defect detection
Opis:
We have developed a deep neural network for casting defect detection. The approach is original because it assumes the use of data related to the casting manufacturing process, i.e. measurement signals from the casting machine, rather than data describing the finished casting, e.g. images. The defects are related to the production of car engine heads made of silumin. In the current research we focused on the detection of defects related to the leakage of the casting. The data came from production plant in Poland. The dataset was unbalanced. It included nearly 38,500 observations, of which only 4% described a leak event. The work resulted in a deep network consisting of 22 layers. We assessed the classification accuracy using a ROC curve, an AUC index and a confusion matrix. The AUC value was 0.97 and 0.949 for the learning and testing dataset, respectively. The model allowed for an ex-post analysis of the casting process. The analysis was based on Shapley values. This makes it possible not only to detect the occurrence of a defect but also to give potential reasons for the appearance of a casting leak.
Źródło:
Advances in Science and Technology. Research Journal; 2022, 16, 5; 120--128
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel data mining approach for defect detection in the printed circuit board manufacturing process
Autorzy:
Bártová, Blanka
Bína, Vladislav
Powiązania:
https://bibliotekanauki.pl/articles/2105321.pdf
Data publikacji:
2022
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
quality management
defect detection
AOI
PCA
PCB
SVM
zarządzanie jakością
wykrywanie defektów
Opis:
This research aims to propose an effective model for the detection of defective Printed Circuit Boards (PCBs) in the output stage of the Surface-Mount Technology (SMT) line. The emphasis is placed on increasing the classification accuracy, reducing the algorithm training time, and a further improvement of the final product quality. This approach combines a feature extraction technique, the Principal Component Analysis (PCA), and a classification algorithm, the Support Vector Machine (SVM), with previously applied Automated Optical Inspection (AOI). Different types of SVM algorithms (linear, kernels and weighted) were tuned to get the best accuracy of the resulting algorithm for separating good-quality and defective products. A novel automated defect detection approach for the PCB manufacturing process is proposed. The data from the real PCB manufacturing process were used for this experimental study. The resulting PCALWSVM model achieved 100 % accuracy in the PCB defect detection task. This article proposes a potentially unique model for accurate defect detection in the PCB industry. A combination of PCA and LWSVM methods with AOI technology is an original and effective solution. The proposed model can be used in various manufacturing companies as a postprocessing step for an SMT line with AOI, either for accurate defect detection or for preventing false calls.
Źródło:
Engineering Management in Production and Services; 2022, 14, 2; 13--25
2543-6597
2543-912X
Pojawia się w:
Engineering Management in Production and Services
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A concept of defect identification with use of texture analysis methods
Koncepcja detekcji defektów powierzchni z zastosowaniem metod analizy obrazów
Autorzy:
Bzymek, A.
Powiązania:
https://bibliotekanauki.pl/articles/328075.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
tekstura
analiza obrazów
detekcja defektów
lokalizacja defektów
texture
image analysis
defect detection
defect localization
Opis:
One of fast developing methods for non destructive testing (NDT) is machine vision. The application of vision systems and image analysis and recognition procedures to variety of problems such as quality control of different products, surface quality estimation and defect identification is very popular nowadays. In the paper short review of applications of vision systems in diagnostics was presented, problem of texture in the images was described, some methods of textured image analysis was enumerated. Finally, the concept of defect identification with the use of image analysis techniques was presented. The purpose was to choose the methods of analysis and recognition which make possible to detect anomalies without the necessity of defect database and texture database creation. Two methods of texture analysis - GLCM and SVD were elaborated and results of algorithm operation were presented. Research were elaborated in Department of Fundamentals of Machinery Design of Silesian University of Technology within the framework of N504403735/32889 project.
Jedną z bardzo szybko rozwijających się gałęzi badań nieniszczących jest wizja maszynowa. Metoda ta bazuje na technikach przetwarzania, analizy i rozpoznawania obrazów. Znajduje ona szerokie zastosowanie w systemach kontroli jakości m.in. do oceny stanu powierzchni oraz detekcji i identyfikacji defektów. W artykule przedstawiono krótki opis systemów wizyjnych oraz przegląd ich zastosowań w diagnostyce maszyn. Przedstawiony został również problem, związany z analizą obrazów, jakim jest występowanie tekstury na obserwowanych powierzchniach. W artykule opisane zostały wybrane metody analizy obrazów reprezentujących teksturę oraz przedstawiono koncepcję detekcji i lokalizacji defektów na takich obrazach. Badania zostały przeprowadzone w Katedrze Podstaw Konstrukcji Maszyn w ramach projektu N504403735/32889.
Źródło:
Diagnostyka; 2011, 2(58); 53-60
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new method for automatic defects detection and diagnosis in rolling element bearings using Wald test
Autorzy:
Chiter, A.
Zegadi, R.
El’Hadi Bekka, R.
Felkaoui, A.
Powiązania:
https://bibliotekanauki.pl/articles/280116.pdf
Data publikacji:
2018
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
diagnosis
detection
rolling element bearing
defect
Wald sequential test
Opis:
To detect and to diagnose, the localized defect in rolling bearings, a statistical model based on the sequential Wald test is established to generate a “hypothetical” signal which takes the state zero in absence of the defect, and the state one if a peak of resonance caused by the defect in the bearing is present. The autocorrelation of this signal allows one to reveal the periodicity of the defect and, consequently, one can establish the diagnosis by comparing the frequency of the defect with the characteristic frequencies of the bearing. The originality of this work is the use of the Wald test in the signal processing domain. Secondly, this method permits the detection without considering the level of noise and the number of observations, it can be used as a support for the Fast Fourier Transform. Finally, the simulated and experimental signals are used to show the efficiency of this method based on the Wald test.
Źródło:
Journal of Theoretical and Applied Mechanics; 2018, 56, 1; 123-135
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Defect Detection Using Deep Learning-Based YOLOv3 in Cross-Sectional Image of Additive Manufacturing
Autorzy:
Choi, Byungjoo
Choi, Yongjun
Lee, Moon-Gu
Kim, Jung-Sub
Lee, Sang-Won
Jeon, Yongho
Powiązania:
https://bibliotekanauki.pl/articles/2048889.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
additive manufacturing
deposition defect
data augmentation
YOLOv3
object detection
Opis:
Deposition defects like porosity, crack and lack of fusion in additive manufacturing process is a major obstacle to commercialization of the process. Thus, metallurgical microscopy analysis has been mainly conducted to optimize process conditions by detecting and investigating the defects. However, these defect detection methods indicate a deviation from the operator’s experience. In this study, artificial intelligence based YOLOv3 of object detection algorithm was applied to avoid the human dependency. The algorithm aims to automatically find and label the defects. To enable the aim, 80 training images and 20 verification images were prepared, and they were amplified into 640 training images and 160 verification images using augmentation algorithm of rotation, movement and scale down, randomly. To evaluate the performance of the algorithm, total loss was derived as the sum of localization loss, confidence loss, and classification loss. In the training process, the total loss was 8.672 for the initial 100 sample images. However, the total loss was reduced to 5.841 after training with additional 800 images. For the verification of the proposed method, new defect images were input and then the mean Average Precision (mAP) in terms of precision and recall was 0.3795. Therefore, the detection performance with high accuracy can be applied to industry for avoiding human errors.
Źródło:
Archives of Metallurgy and Materials; 2021, 66, 4; 1037-1041
1733-3490
Pojawia się w:
Archives of Metallurgy and Materials
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic defect detection and characterization by thermographic images based on damage classifiers evaluation
Autorzy:
Dinardo, Giuseppe
Fabbiano, Laura
Tamborrino, Rosanna
Vacca, Gaetano
Powiązania:
https://bibliotekanauki.pl/articles/221850.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
thermography
non-destructive testing
thermal barrier coatings
image segmentation
uncertainty analysis
Opis:
In the framework of non-destructive evaluation (NDE), an accurate and precise characterization of defects is fundamental. This paper proposes a novel method for characterization of partial detachment of thermal barrier coatings from metallic surfaces, using the long pulsed thermography (LPT). There exist many applications, in which the LPT technique provides clear and intelligible thermograms. The introduced method comprises a series of post-processing operations of the thermal images. The purpose is to improve the linear fit of the cooling stage of the surface under investigation in the logarithmic scale. To this end, additional fit parameters are introduced. Such parameters, defined as damage classifiers, are represented as image maps, allowing for a straightforward localization of the defects. The defect size information provided by each classifier is, then, obtained by means of an automatic segmentation of the images. The main advantages of the proposed technique are the automaticity (due to the image segmentation procedures) and relatively limited uncertainties in the estimation of the defect size.
Źródło:
Metrology and Measurement Systems; 2020, 27, 2; 219-242
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wykorzystanie badań nieniszczących do wykrywania defektów w skórach naturalnych
Using the non-destructive testing to defect detection in leathers
Autorzy:
Dudzik, S.
Chudzik, S.
Powiązania:
https://bibliotekanauki.pl/articles/266357.pdf
Data publikacji:
2017
Wydawca:
Politechnika Gdańska. Wydział Elektrotechniki i Automatyki
Tematy:
garbowanie skór
aktywna termografia w podczerwieni
lokalne progowanie adaptacyjne
leather tanning
active infrared thermography
locally adaptive thresholding
Opis:
W pracy zaprezentowano wyniki badań, mających na celu ocenę możliwości wykrywania defektów w skórach naturalnych, z wykorzystaniem aktywnej termografii w podczerwieni. W badaniach użyto czterech próbek skór zawierających wady powstałe w procesie garbowania. Próbki skóry nagrzewano z wykorzystaniem lampy halogenowej i jednocześnie prowadzono rejestrację sekwencji termogramów ich powierzchni. Zarejestrowane termogramy poddano następnie analizie z wykorzystaniem zaawansowanych algorytmów przetwarzania obrazów. W wyniku badań stwierdzono, że aktywna termografia w podczerwieni pozwala na wykrywanie defektów w skórach naturalnych, które nie byłyby możliwe do wykrycia metodami stosowanymi dotychczas. Dodatkowo, zastosowanie zaawansowanych metod przetwarzania obrazów pozwoliło na zautomatyzowanie procesu wykrywania wad.
Natural leathers usually have different defects, resulting from them during the life of the animal, or due to improper skin removal and preservation of leathers. Recognition of defects allows you to assess the quality and determine the suitability of the leathers for particular purposes as well as. In the most commonly used method, the examined leather is spread on a table or a hanger and a subjective evaluation of the defects affecting the homogeneous structure of the hulled natural leather is made by means of the human senses (i.e. the human eye or the human touch). In this paper an experimental study was carried out to evaluate the possibility of detecting defects, arising from tanning, and further processing of natural leather. In experiments, the investigated leather sample was heated and the transient temperature field was recorded by an infrared camera. Finally, the thermograms of the leather surface were processed and the diagnostic information about the defect presence was obtained. In the paper it was found the active thermography is very useful for detection of defects in leathers. Furthermore, the application of advanced image processing methods allowed to fully automate the detection process.
Źródło:
Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej; 2017, 54; 39-42
1425-5766
2353-1290
Pojawia się w:
Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie metod progowania lokalnego do wykrywania defektów z użyciem termografii aktywnej
Application of local thresholding algorithms for defect detection using active thermography
Autorzy:
Dudzik, S.
Sochacka, O.
Powiązania:
https://bibliotekanauki.pl/articles/267010.pdf
Data publikacji:
2018
Wydawca:
Politechnika Gdańska. Wydział Elektrotechniki i Automatyki
Tematy:
termografia aktywna
badania nieniszczące
wykrywanie defektów
progowanie lokalne
active thermography
non-destructive testing
defect detection
local thresholding
Opis:
W pracy zaprezentowano zastosowanie metod progowania lokalnego do wykrywania defektów z użyciem termografii aktywnej. Przeprowadzono badania eksperymentalne polegające na rejestracji sekwencji termogramów powierzchni badanej próbki materiału dla dwóch wymuszeń cieplnych oraz dwóch faz procesu wymiany ciepła (faza nagrzewania i faza stygnięcia). Sekwencje termogramów uzyskane w badaniach eksperymentalnych zostały poddane binaryzacji z wykorzystaniem lokalnych metod progowania. Do oszacowania efektywności wykrywania defektów za pomocą zaproponowanych metod, zastosowano kryteria oparte na pojęciu błędu klasyfikacji w obszarze defektów i obszarze tła. Na podstawie badań stwierdzono, że największą dokładność uzyskuje się z stosując zmodyfikowaną metodę Bradleya.
The paper presents the application of local thresholding methods for defect detection using active thermography. Experimental studies were performed involving the recording of the sequence of thermograms of the surface of the tested material sample. Experiments were conducted for two different thermal excitations and two phases of the heat transfer process (i.e. heating phase and cooling phase). The thermograms from sequences obtained in experimental studies were then binarized using local thresholding methods. Three following methods were employed: modified Bradley method, median method and Gaussian weighted mean method. To assess the accuracy of defect detection using the proposed algorithms, the criteria based on the concept of classification error in the defected and non-defected areas were applied. In this work it was found that the most accurate method is the modified Bradley method.
Źródło:
Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej; 2018, 59; 43-46
1425-5766
2353-1290
Pojawia się w:
Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection of thinning of homogeneous material using active thermography and classification trees
Autorzy:
Dudzik, Sebastian
Dudek, Grzegorz
Powiązania:
https://bibliotekanauki.pl/articles/1848987.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
active thermography
classification tree
defect detection and characterization
material thinning detection
Opis:
Active thermography is an efficient tool for defect detection and characterization as it does not change the properties of tested materials. The detection and characterization process involves heating a sample and then analysing the thermal response. In this paper, a long heating pulse was used on samples with a low thermal diffusivity and artificially created holes of various depths. As a result of the experiments, heating and cooling curves were obtained. These curves, which describe local characteristics of the material, are recognized using a classification tree and divided into categories depending on the material thickness (hole depths). Two advantages of the proposed use of classification trees are: an in-built mechanism for feature selection and a strong reduction in the dimensions of the pattern. Based on the experimental study, it can be concluded that classification trees are a useful tool for the thinning detection of homogeneous material.
Źródło:
Metrology and Measurement Systems; 2021, 28, 1; 89-105
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wykrywanie defektów z wykorzystaniem termografii aktywnej i algorytmu k-średnich
Detection of Defects Using Active Thermography and k-Means Algorithm
Autorzy:
Dudzik, Sebastian
Powiązania:
https://bibliotekanauki.pl/articles/275938.pdf
Data publikacji:
2019
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
algorytm k-średnich
wykrywanie defektów
termografia aktywna
k-means algorithm
defect detection
active thermography
Opis:
W pracy przedstawiono nową metodę wykrywania defektów materiałowych z wykorzystaniem termografii aktywnej. W celu zwiększenia kontrastu cieplnego dokonano przetwarzania wstępnego zarejestrowanej sekwencji termogramów metodami morfologii matematycznej. Do wykrywania defektów zastosowano algorytm k-średnich. W pracy zbadano wpływ miary odległości używanej w opisywanym algorytmie oraz doboru danych wejściowych na efektywność opisywanej metody. Eksperyment przeprowadzono dla próbki wykonanej z kompozytu zbrojonego włóknem węglowym (CFRP). W badaniach stwierdzono, że najmniejsze błędy wykrywania defektów za pomocą opisywanej metody uzyskuje się dla kwadratowej odległości euklidesowej.
The paper presents a new method of detecting material defects using active thermography. In order to increase the thermal contrast, preprocessing of the recorded sequence of thermograms was carried out using mathematical morphology methods. The k-means algorithm was used to detect defects. The work examined the impact of distance measure used in the described algorithm and the selection of input data on the effectiveness of the described method. The experiment was carried out for a sample made of carbon fiber reinforced composite (CFRP). Studies have shown that the smallest errors in defect detection using the described method are obtained for the square Euclidean distance.
Źródło:
Pomiary Automatyka Robotyka; 2019, 23, 3; 11-15
1427-9126
Pojawia się w:
Pomiary Automatyka Robotyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Diagnostic methods of detecting defects within the material with the use of active infrared thermovision
Autorzy:
Grochalski, K.
Peta, K.
Powiązania:
https://bibliotekanauki.pl/articles/94152.pdf
Data publikacji:
2017
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
active thermovision
thermal images analysis
diagnostic
external forcing heat
defect detection
termowizja aktywna
analiza obrazów termowizyjnych
diagnostyka
zewnętrzne wymuszanie ciepła
wykrywanie usterek
Opis:
Article presents the methods of detecting defects within material with the use of active infrared thermovision. During the study ABS and PVC samples were used inside which internal structure defects and defects of glue conjunction between components were modeled. During combining composite materials with the use of glue joints, there is a problem with homogenous distribution of the glue layer on the surface of an element, which results in the creation of defects in joint structure and the decline of active surface of adhesion forces on the combined materials. It is then necessary to control the quality of the conjunction between the glued surfaces. The use of non-contact diagnostic methods allows to analyze a larger surface which conditions in more efficient quality control process. In the study, external heat excitation was used - optical excitation with periodic variable signal (LockIn method) and unit step excitation (Pulse method). The methods of analysis of the obtained thermograms are presented.
Źródło:
Archives of Mechanical Technology and Materials; 2017, 37; 41-44
2450-9469
Pojawia się w:
Archives of Mechanical Technology and Materials
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Kontrast filtrowany w charakteryzacji wad materiałowych metodą aktywnej termografii
Filtered contrast in defect characterization using active infrared thermography
Autorzy:
Gryś, S.
Minkina, W.
Powiązania:
https://bibliotekanauki.pl/articles/152646.pdf
Data publikacji:
2010
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
badania nieniszczące
aktywna termografia
charakteryzacja defektów
propagacja rozkładów
symulacja Monte Carlo
nondestructive testing
active thermography
defect detection and characterization
propagation of distributions
Monte Carlo simulation
Opis:
W artykule przedstawiono możliwości zastosowania "kontrastu filtrowanego" do wykrywania i charakteryzacji defektów w materiale metodą aktywnej termografii. Do wyznaczenia "kontrastu filtrowanego" nie jest wymagana znajomość obszaru referencyjnego bez defektu oraz jest mniej wrażliwy na niejednorodność napromienienia powierzchni materiału w porównaniu do klasycznych rodzajów kontrastów cieplnych. Eksperyment przeprowadzono na stanowisku wyposażonym w kamerę ThermaCAM PM 595 oraz kartę rejestracji danych. Oszacowano wpływ parametrów cieplnych badanego materiału i defektu oraz parametru filtru wygładzającego, niezbędnego do implementacji idei kontrastu filtrowanego, na niepewność estymacji głębokości defektów. W analizie zastosowano zasadę propagacji rozkładów prawdo-podobieństwa i symulację Monte Carlo.
The paper presents the possibility of the use of new kind of thermal contrast in subsurface defects detection [1,3-6]. It allows detecting defects taking advantages [2,6] of an active thermography - Table 1. In opposition to known definitions of the thermal contrast [1], a defect-free area is no necessary and this contrast is less sensitive to nonuniformity of heat disposal to the material surface. The measurements were per-formed on a setup presented in Fig. 1 [7]. A special sample of Plexiglas was made with bottom-holes simulating defects - Figs 2 and 3. The material parameters - Table 2, were taken from [1]. The step heating was chosen as heat excitation. Exemplary, raw and processed thermograms for symmetrical and asymmetrical heat disposal are shown in Figs 4 and 5. The influence of the parameter B of the smoothing filter, thermal parameters of the tested material and defect on expanded uncertainty of determination of defect depth were analyzed. Due to significant complexity of the model the numerical method, i.e. Monte Carlo simulation was applied. According to this procedure [9,10] an expectation and 95% coverage intervals are presented in Tab 4 and 5. In the Table 6 there is fixed if the 95% coverage interval contains the true value of depth. For the inspected sample, B=10, and assumed accuracy of evaluation of diffusivity [8] of Plexiglas, the accuracy of the method does not exceed 20%. The optimal value of B corresponds to the diameter of defects. This aspect will be examined in further work.
Źródło:
Pomiary Automatyka Kontrola; 2010, R. 56, nr 8, 8; 893-896
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deblurring approach for motion camera combining FFT with α-confidence goal optimization
Autorzy:
Huang, Lve
Wu, Lushen
Xiao, Wenyan
Peng, Qingjin
Powiązania:
https://bibliotekanauki.pl/articles/174045.pdf
Data publikacji:
2020
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
image deblurring
fast motion camera
confidence goal optimization
fast Fourier transform
high-railway defect detection
Opis:
Sharp images ensure success in the object detection and recognition from state-of-art deep learning methods. When there is a fast relative motion between the camera and the object being imaged during exposure, it will necessarily result in blurred images. To deblur the images acquired under the camera motion for high-quality images, a deblurring approach with relatively simple calculation is proposed. An accurate estimation method of point spread function is firstly developed by performing the Fourier transform twice. Artifacts caused by image direct deconvolution are then reduced by predicting the image boundary region, and the deconvolution model is optimized by an α-confidence statistics algorithm based on the greyscale consistency of the image adjacent columns. The proposed deblurring approach is finally carried out on both the synthetic-blurred images and the real-scene images. The experiment results demonstrate that the proposed image deblurring approach outperforms the existing methods for the images that are seriously blurred in direction motion.
Źródło:
Optica Applicata; 2020, 50, 2; 185-198
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 43

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