scholarly journals Wavelet-based multiresolution analysis coupled with deep learning to efficiently monitor cracks in concrete

2021 ◽  
Vol 15 (58) ◽  
pp. 33-47
Author(s):  
Ahcene Arbaoui ◽  
Abdeldjalil Ouahabi ◽  
Sebastien Jacques ◽  
Madina Hamiane
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Multiresolution Analysis ◽  
Early Stage ◽  
Concrete Surface ◽  
Automatic Identification ◽  
Engineering Structures ◽  
Optical Inspection ◽  
Formation Of Cracks ◽  
Non Destructive

This paper proposes an efficient methodology to monitor the formation of cracks in concrete after non-destructive ultrasonic testing of a structure. The objective is to be able to automatically detect the initiation of cracks early enough, i.e. well before they are visible on the concrete surface, in order to implement adequate maintenance actions on civil engineering structures. The key element of this original approach is the wavelet-based multiresolution analysis of the ultrasonic signal received from a sample or a specimen of the studied material subjected to several types of solicitation. This analysis is finally coupled to an automatic identification scheme of the types of cracks based on artificial neural networks (ANNs), and in particular deep learning by convolutional neural networks (CNNs); a technology today at the cutting edge of machine learning, in particular for all applications of pattern recognition. Wavelet-based multiresolution analysis does not add any value in detecting fractures in concrete visible by optical inspection. However, the results of its implementation coupled with different CNN architectures show cracks in concrete can be identified at an early stage with a very high accuracy, i.e. around 99.8%, and a loss function of less than 0.1, regardless of the implemented learning architecture.

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.

A Non-Destructive Testing Based on Electromagnetic Measurements and Neural Networks for the Inspection of Concrete Structures

2011 ◽  
Vol 301-303 ◽  
pp. 597-602 ◽  
Author(s):  
Naasson P. de Alcantara ◽  
Danilo C. Costa ◽  
Diego S. Guedes ◽  
Ricardo V. Sartori ◽  
Paulo S. S. Bastos
Keyword(s):  
Neural Networks ◽  
Concrete Structures ◽  
Concrete Surface ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Electromagnetic Devices ◽  
Electromagnetic Measurements ◽  
Steel Bars ◽  
Non Destructive ◽  
The Magnetic Field

This paper presents a new non-destructive testing (NDT) for reinforced concrete structures, in order to identify the components of their reinforcement. A time varying electromagnetic field is generated close to the structure by electromagnetic devices specially designed for this purpose. The presence of ferromagnetic materials (the steel bars of the reinforcement) immersed in the concrete disturbs the magnetic field at the surface of the structure. These field alterations are detected by sensors coils placed on the concrete surface. Variations in position and cross section (the size) of steel bars immersed in concrete originate slightly different values for the induced voltages at the coils.. The values ​​for the induced voltages were obtained in laboratory tests, and multi-layer perceptron artificial neural networks with Levemberg-Marquardt training algorithm were used to identify the location and size of the bar. Preliminary results can be considered very good.

scholarly journals Thermal-based early breast cancer detection using inception V3, inception V4 and modified inception MV4

2021 ◽  
Author(s):  
Mohammed Abdulla Salim Al Husaini ◽  
Mohamed Hadi Habaebi ◽  
Teddy Surya Gunawan ◽  
Md Rafiqul Islam ◽  
Elfatih A. A. Elsheikh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Early Stage ◽  
Computational Cost ◽  
Optimization Methods ◽  
Optimization Method ◽  
Deep Convolutional Neural Networks ◽  
Breast Thermography

AbstractBreast cancer is one of the most significant causes of death for women around the world. Breast thermography supported by deep convolutional neural networks is expected to contribute significantly to early detection and facilitate treatment at an early stage. The goal of this study is to investigate the behavior of different recent deep learning methods for identifying breast disorders. To evaluate our proposal, we built classifiers based on deep convolutional neural networks modelling inception V3, inception V4, and a modified version of the latter called inception MV4. MV4 was introduced to maintain the computational cost across all layers by making the resultant number of features and the number of pixel positions equal. DMR database was used for these deep learning models in classifying thermal images of healthy and sick patients. A set of epochs 3–30 were used in conjunction with learning rates 1 × 10–3, 1 × 10–4 and 1 × 10–5, Minibatch 10 and different optimization methods. The training results showed that inception V4 and MV4 with color images, a learning rate of 1 × 10–4, and SGDM optimization method, reached very high accuracy, verified through several experimental repetitions. With grayscale images, inception V3 outperforms V4 and MV4 by a considerable accuracy margin, for any optimization methods. In fact, the inception V3 (grayscale) performance is almost comparable to inception V4 and MV4 (color) performance but only after 20–30 epochs. inception MV4 achieved 7% faster classification response time compared to V4. The use of MV4 model is found to contribute to saving energy consumed and fluidity in arithmetic operations for the graphic processor. The results also indicate that increasing the number of layers may not necessarily be useful in improving the performance.

scholarly journals Deep learning in Dermatology for skin Diseases Detection.

2020 ◽  
Vol 8 (6) ◽  
pp. 3929-3933
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Early Detection ◽  
Cell Carcinoma ◽  
Skin Diseases ◽  
Early Stage ◽  
Medical Field ◽  
Home Remedy ◽  
Disease Symptoms ◽  
Skin Cancers

Dermatology is a medical field that treats skin health and diseases. People feeling disease symptoms of an affecting the skin must consult a dermatologist if this stipulation does not respond to home remedy. Early detection and treatment can correct most skin disorders. Basal Cell Carcinoma (BCC), Melanoma and Squamous Cell Carcinoma (SCC) are typically appearing type of skin cancers. The purpose of this effort is to provide a system that can be deployed to classify dermatoscopic images to predict skin diseases with early detection and higher accuracy . This work is a concrete effort to accomplish higher degree of accuracy for clinical usage by implementing advances in soft computing and image processing like deep learning and in-depth neural networks in an early stage for 7 class classification for HAM10000 dataset.

scholarly journals Plant Diseases Identification through a Discount Momentum Optimizer in Deep Learning

2021 ◽  
Vol 11 (20) ◽  
pp. 9468
Author(s):  
Yunyun Sun ◽  
Yutong Liu ◽  
Haocheng Zhou ◽  
Huijuan Hu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Adaptive Learning ◽  
Learning Rate ◽  
Plant Diseases ◽  
Stochastic Gradient Descent ◽  
Automatic Identification ◽  
Deep Convolutional Neural Networks ◽  
Adaptive Learning Rate

Deep learning proves its promising results in various domains. The automatic identification of plant diseases with deep convolutional neural networks attracts a lot of attention at present. This article extends stochastic gradient descent momentum optimizer and presents a discount momentum (DM) deep learning optimizer for plant diseases identification. To examine the recognition and generalization capability of the DM optimizer, we discuss the hyper-parameter tuning and convolutional neural networks models across the plantvillage dataset. We further conduct comparison experiments on popular non-adaptive learning rate methods. The proposed approach achieves an average validation accuracy of no less than 97% for plant diseases prediction on several state-of-the-art deep learning models and holds a low sensitivity to hyper-parameter settings. Experimental results demonstrate that the DM method can bring a higher identification performance, while still maintaining a competitive performance over other non-adaptive learning rate methods in terms of both training speed and generalization.

scholarly journals Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5280
Author(s):  
Balakrishnan Ramalingam ◽  
Rajesh Elara Mohan ◽  
Sathian Pookkuttath ◽  
Braulio Félix Gómez ◽  
Charan Satya Chandra Sairam Borusu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Built Environment ◽  
Object Detection ◽  
Real Time ◽  
Monitoring System ◽  
Early Stage ◽  
Control Measures ◽  
Dependent Tasks ◽  
And Control

Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy.

scholarly journals Intelligent fault detection of electrical assemblies using hierarchical convolutional networks for supporting automatic optical inspection systems

2020 ◽  
Vol 9 (2) ◽  
pp. 363-374
Author(s):  
Alida Ilse Maria Schwebig ◽  
Rainer Tutsch
Keyword(s):  
Neural Networks ◽  
Recognition Accuracy ◽  
Early Stage ◽  
Classification Systems ◽  
Inspection System ◽  
Automotive Electronics ◽  
Automatic Optical Inspection ◽  
Convolutional Networks ◽  
Optical Inspection ◽  
Inspection Systems

Abstract. Electrical assemblies are the core of many electronic devices and therefore represent a crucial part of the overall product, which must be carefully checked before integration into its functional environment. For this reason, automatic optical inspection systems are required in electronic manufacturing to detect visible errors in products at an early stage. In particular, the automotive electronics production area is one of the sectors in which quality assurance has uppermost priority, as undetected defects can pose a danger to life. However, most optical inspection processes still have error slippage rates, which are responsible for delivering faulty electrical assemblies to customers. Therefore, this article shows how an application strategy of deep learning, based on neural networks, can be combined with an automatic optical inspection system to further increase the recognition accuracy of the process. The additional use of artificial intelligence supported classification systems provides a way to find out the exact details about the manufacturing-related errors of electrical assemblies. However, due to the high number of different error categories, a single classification algorithm is usually not sufficient to provide reliable visual inspection results with high robustness against error slip. For this reason, a hierarchical model with multiple classifiers is proposed in this article. The principle is based on the hierarchical description of the quality status and fault types using several combined neural networks. In this context, the original classification task is distributed over different subnetworks. These subnetworks, which interact as an overall model, only verify certain error and quality features of the electrical assemblies, which means that higher recognition accuracy and robustness can be achieved compared to a single network.

scholarly journals Attention-based YOLOv4 Algorithm in Non-destructive Radiographic Testing for Civic Aviation Maintenance

2021 ◽  
Author(s):  
Zhi-Hao Chen ◽  
Jyh-Ching Juang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Defect Detection ◽  
Attention Mechanism ◽  
Civil Aviation ◽  
Automatic Identification ◽  
Learning Tools ◽  
Destructive Testing ◽  
Aviation Maintenance ◽  
Non Destructive

The aim of this paper is to use deep learning tools to innovate pre-trained object detection models to improve the accuracy of non-destructive testing (NDT) of civil aviation maintenance. First, this thesis classifies object defects for NDT, such as cracks, undercut, etc. Nowadays, thesis surveys innovation deep-learning methods technology is used to improve the defect detection performance inferencing capability, increase the accuracy and efficiency of automatic identification which in enhanced the safety and reliability of aircraft fuselage in future, mark hidden cracks and solve the challenges that cannot be identified by manual inspection. Second, recent mainstream techniques the YOLOv4 neural network to the graphics card GPU core operator to speed up the recognition of defect images is being applied to the non-destructive inspection process of aircraft maintenance on A, C and D-Level, fully validating the deep learning model's powerful defect detection target capability. The attention-based YOLOv4 algorithm is improved by applying a one-stage attention mechanism to the YOLOv4, thereby improving the accuracy of the innovation model. Finally, thesis improved YOLOv4 based on an attention mechanism is proposed for object detection NDT via the deep learning method to effectively improve and shorten the inspection anomaly detection method for automatic detection sensor systems.

scholarly journals Deep Learning Model for Plant Disease Detection

2020 ◽  
Vol 9 (1) ◽  
pp. 750-754
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Early Detection ◽  
Plant Disease ◽  
Early Stage ◽  
Learning Model ◽  
Plant Diseases ◽  
Convolution Neural Networks ◽  
Deep Learning Model

Food is one of the basic needs of human being. We know that the population is rising enormously.so it is more important to feed such a huge population. But nowadays plants are largely affected with various types of diseases. If proper care should not be taken then it will show effect on quality of food products, quantity and finally on productivity of crops.. so, Early detection of plant disease is very essential, but it is very hard to farmers to monitor the crops manually it takes more processing time, huge amount of work, expensive and need expertised persons. Automatic detection of plant diseases helps the farmers to monitor the large fields easily,because our approach of using convolution neural networks provides a chance to discover diseases at the very early stage. By using Image Processing and machine learning models we can detect the plant diseases automatically but the accuracy is very less, early detection is also a major challenge. With the modern advanced developments in deep learning, in our project we have implemented the convolution neural networks(CNN) which comprises of different layers,by using those layers we can automatically detect and classify the diseases present in the plants. High Classification accuracy and more processing speed are the main advantages of our approach. After training the model on color, grayscale and segmented datasets our deep learning model will be capable of classifying a large number of different diseases and our project gives us the name of the disease that the plant has with its confidence level and also provides remedies for corresponding diseases

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