Non‐destructive detection of blueberry skin pigments and intrinsic fruit qualities based on deep learning

2020 ◽  
Author(s):  
Changhong Mu ◽  
Zebin Yuan ◽  
Xiuqin Ouyang ◽  
Pu Sun ◽  
Bo Wang
Keyword(s):  
Deep Learning ◽  
Non Destructive

Multi-Mineral Segmentation of SEM Images Using Deep Learning Techniques

2021 ◽  
Author(s):  
Vladislav Vasilevich Alekseev ◽  
Denis Mihaylovich Orlov ◽  
Dmitry Anatolevich Koroteev
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Pore Space ◽  
Rock Physics ◽  
Sem Images ◽  
Digital Rock Physics ◽  
Learning Techniques ◽  
Digital Core ◽  
Non Destructive

Abstract The approaches of building and methods of using the digital core are currently developing rapidly. The use of these methods makes it possible to obtain petrophysical information by non-destructive methods quickly. Digital rock physics includes two main stages: constructing models and modeling various physical processes on the obtained models. Our work proposes using deep learning methods for mineral and pore space segmentation instead of classical methods such as threshold image processing. Deep neural networks have long been able to show their advantages in many areas of computer vision. This paper proposes and tests methods that help identify different minerals in images from a scanning electron microscope. We used images of rocks of the Achimov formation, which are arkoses, as samples. We tested various deep neural networks such as LinkNet, U-Net, ResUNet, and pix2pix and identified those that performed best in segmentation.

scholarly journals Deep Learning-Based Classification of Weld Surface Defects

2019 ◽  
Vol 9 (16) ◽  
pp. 3312 ◽  
Author(s):  
Zhu ◽  
Ge ◽  
Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Surface Defects ◽  
Recognition Method ◽  
High Level ◽  
Intelligent Recognition ◽  
Non Destructive

In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.

scholarly journals Wireless power transfer-based eddy current non-destructive testing using a flexible printed coil array

2020 ◽  
Vol 378 (2182) ◽  
pp. 20190579
Author(s):  
Lawal Umar Daura ◽  
GuiYun Tian ◽  
Qiuji Yi ◽  
Ali Sophian
Keyword(s):  
Deep Learning ◽  
Eddy Current ◽  
Feature Fusion ◽  
Non Destructive Testing ◽  
Wireless Power ◽  
Destructive Testing ◽  
Multiple Feature ◽  
Excitation Coil ◽  
Dual Resonance ◽  
Non Destructive

Eddy current testing (ECT) has been employed as a traditional non-destructive testing and evaluation (NDT&E) tool for many years. It has developed from single frequency to multiple frequencies, and eventually to pulsed and swept-frequency excitation. Recent progression of wireless power transfer (WPT) and flexible printed devices open opportunities to address challenges of defect detection and reconstruction under complex geometric situations. In this paper, a transmitter–receiver (Tx–Rx) flexible printed coil (FPC) array that uses the WPT approach featuring dual resonance responses for the first time has been proposed. The dual resonance responses can provide multiple parameters of samples, such as defect characteristics, lift-offs and material properties, while the flexible coil array allows area mapping of complex structures. To validate the proposed approach, experimental investigations of a single excitation coil with multiple receiving coils using the WPT principle were conducted on a curved pipe surface with a natural dent defect. The FPC array has one single excitation coil and 16 receiving (Rx) coils, which are used to measure the dent by using 21 C-scan points on the dedicated dent sample. The experimental data were then used for training and evaluation of dual resonance responses in terms of multiple feature extraction, selection and fusion for quantitative NDE. Four features, which include resonant magnitudes and principal components of the two resonant areas, were investigated for mapping and reconstructing the defective dent through correlation analysis for feature selection and feature fusion by deep learning. It shows that deep learning-based multiple feature fusion has outstanding performance for 3D defect reconstruction of WPT-based FPC-ECT. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.

scholarly journals Application of Deep Learning in Infrared Non-Destructive Testing

2018 ◽  
Author(s):  
B. Yousefi ◽  
D. Kalhor ◽  
R. Usamentiaga ◽  
L. Lei ◽  
C. Ibarra-Castanedo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Non Destructive

scholarly journals Instantaneous and non-destructive relative water content estimation from deep learning applied to resonant ultrasonic spectra of plant leaves

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
María Dolores Fariñas ◽  
Daniel Jimenez-Carretero ◽  
Domingo Sancho-Knapik ◽  
José Javier Peguero-Pina ◽  
Eustaquio Gil-Pelegrín ◽  
...  
Keyword(s):  
Deep Learning ◽  
Water Content ◽  
Transmission Coefficient ◽  
Relative Water Content ◽  
Plant Leaves ◽  
Resonant Ultrasound Spectroscopy ◽  
Relative Water ◽  
Resonant Ultrasound ◽  
Non Destructive

Abstract Background Non-contact resonant ultrasound spectroscopy (NC-RUS) has been proven as a reliable technique for the dynamic determination of leaf water status. It has been already tested in more than 50 plant species. In parallel, relative water content (RWC) is highly used in the ecophysiological field to describe the degree of water saturation in plant leaves. Obtaining RWC implies a cumbersome and destructive process that can introduce artefacts and cannot be determined instantaneously. Results Here, we present a method for the estimation of RWC in plant leaves from non-contact resonant ultrasound spectroscopy (NC-RUS) data. This technique enables to collect transmission coefficient in a [0.15–1.6] MHz frequency range from plant leaves in a non-invasive, non-destructive and rapid way. Two different approaches for the proposed method are evaluated: convolutional neural networks (CNN) and random forest (RF). While CNN takes the entire ultrasonic spectra acquired from the leaves, RF only uses four relevant parameters resulted from the transmission coefficient data. Both methods were tested successfully in Viburnum tinus leaf samples with Pearson’s correlations between 0.92 and 0.84. Conclusions This study showed that the combination of NC-RUS technique with deep learning algorithms is a robust tool for the instantaneous, accurate and non-destructive determination of RWC in plant leaves.

scholarly journals A Transfer Residual Neural Network Based on ResNet-34 for Detection of Wood Knot Defects

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 212
Author(s):  
Mingyu Gao ◽  
Dawei Qi ◽  
Hongbo Mu ◽  
Jianfeng Chen
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Detection Accuracy ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Feature Extractor ◽  
Testing Technology ◽  
Excessive Consumption ◽  
Non Destructive ◽  
Deep Learning Model

In recent years, due to the shortage of timber resources, it has become necessary to reduce the excessive consumption of forest resources. Non-destructive testing technology can quickly find wood defects and effectively improve wood utilization. Deep learning has achieved significant results as one of the most commonly used methods in the detection of wood knots. However, compared with convolutional neural networks in other fields, the depth of deep learning models for the detection of wood knots is still very shallow. This is because the number of samples marked in the wood detection is too small, which limits the accuracy of the final prediction of the results. In this paper, ResNet-34 is combined with transfer learning, and a new TL-ResNet34 deep learning model with 35 convolution depths is proposed to detect wood knot defects. Among them, ResNet-34 is used as a feature extractor for wood knot defects. At the same time, a new method TL-ResNet34 is proposed, which combines ResNet-34 with transfer learning. After that, the wood knot defect dataset was applied to TL-ResNet34 for testing. The results show that the detection accuracy of the dataset trained by TL-ResNet34 is significantly higher than that of other methods. This shows that the final prediction accuracy of the detection of wood knot defects can be improved by TL-ResNet34.

scholarly journals Classification of soybean tempe quality using deep learning

2021 ◽  
Vol 924 (1) ◽  
pp. 012022
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
I Prakoso ◽  
V Liana ◽  
M R Fauzy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Low Cost ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Traditional Food ◽  
Quality Classification ◽  
Non Destructive

Abstract Tempe is a traditional food originating from Indonesia, which is made from the fermentation process of soybean using Rhizopus mold. The purpose of this study was to classify three quality levels of soybean tempe i.e., fresh, consumable, and non-consumable using a convolutional neural network (CNN) based deep learning. Four types of pre-trained networks CNN were used in this study i.e. SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The sensitivity analysis showed the highest quality classification accuracy of soybean tempe was 100% can be achieved when using AlexNet with SGDm optimizer and learning rate of 0.0001; GoogLeNet with Adam optimizer and learning rate 0.0001, GoogLeNet with RMSProp optimizer, and learning rate 0.0001, ResNet50 with Adam optimizer and learning rate 0.00005, ResNet50 with Adam optimizer and learning rate 0.0001, and SqueezeNet with RSMProp optimizer and learning rate 0.0001. In further testing using testing-set data, the classification accuracy based on the confusion matrix reached 98.33%. The combination of the CNN model and the low-cost digital commercial camera can later be used to detect the quality of soybean tempe with the advantages of being non-destructive, rapid, accurate, low-cost, and real-time.

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