scholarly journals Pavement Crack Detection Using Convolutional Neural Network

10.29007/h4k6 ◽  
2020 ◽  
Author(s):  
Alaa Sheta ◽  
Hamza Turabieh ◽  
Sultan Aljahdali ◽  
Abdulaziz Alangari
Keyword(s):  
Neural Network ◽  
New York ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Stable Condition ◽  
Image Data ◽  
Data Sets ◽  
Data Set ◽  
Data Division ◽  
The Usa

Automating the process of detecting pavement cracks became a challenge mission. In the last few decades, many methods were proposed to solve this problem. The reason is that maintaining a stable condition of roads is essential for the safety of people and public properties. It was reported that maintaining one mile of roads in New York City in the USA might cost from four to ten thousand dollars. In this paper, we explore our initial idea of developing a lightweight Convolutional Neural Network (CNN or ConvNet) model that can be used to detect pavement cracks. The proposed CNN was trained using the AigleRN data set, which contains 400 images of road cracks of 480×320 resolution. The proposed lightweight CNN architecture performed a better fitting to the image data set due to the reduction in the number of parameters. The proposed CNN was capable of detecting cracks with a various number of sample images. We simulated the CNN architecture over different sizes of training/testing (i.e., 90/10, 80/20, and 70/30) data sets for 11 runs. The obtained results show that 90/10 data division for training and testing is outperformed other categories with an average accuracy of 97.27%.

scholarly journals A New Classification Method in Ultrasound Images of Benign and Malignant Thyroid Nodules Based on Transfer Learning and Deep Convolutional Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9 ◽  
Author(s):  
Weibin Chen ◽  
Zhiyang Gu ◽  
Zhimin Liu ◽  
Yaoyao Fu ◽  
Zhipeng Ye ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Thyroid Nodules ◽  
Ultrasound Image ◽  
Image Data ◽  
Data Sets ◽  
Thyroid Ultrasound ◽  
Joint Training ◽  
Secondary Transfer

Thyroid nodule is a clinical disorder with a high incidence rate, with large number of cases being detected every year globally. Early analysis of a benign or malignant thyroid nodule using ultrasound imaging is of great importance in the diagnosis of thyroid cancer. Although the b-mode ultrasound can be used to find the presence of a nodule in the thyroid, there is no existing method for an accurate and automatic diagnosis of the ultrasound image. In this pursuit, the present study envisaged the development of an ultrasound diagnosis method for the accurate and efficient identification of thyroid nodules, based on transfer learning and deep convolutional neural network. Initially, the Total Variation- (TV-) based self-adaptive image restoration method was adopted to preprocess the thyroid ultrasound image and remove the boarder and marks. With data augmentation as a training set, transfer learning with the trained GoogLeNet convolutional neural network was performed to extract image features. Finally, joint training and secondary transfer learning were performed to improve the classification accuracy, based on the thyroid images from open source data sets and the thyroid images collected from local hospitals. The GoogLeNet model was established for the experiments on thyroid ultrasound image data sets. Compared with the network established with LeNet5, VGG16, GoogLeNet, and GoogLeNet (Improved), the results showed that using GoogLeNet (Improved) model enhanced the accuracy for the nodule classification. The joint training of different data sets and the secondary transfer learning further improved its accuracy. The results of experiments on the medical image data sets of various types of diseased and normal thyroids showed that the accuracy rate of classification and diagnosis of this method was 96.04%, with a significant clinical application value.

scholarly journals Core looseness fault identification model based on Mel spectrogram-CNN

2021 ◽  
Vol 2137 (1) ◽  
pp. 012060
Author(s):  
Ping He ◽  
Yong Li ◽  
Shoulong Chen ◽  
Hoghua Xu ◽  
Lei Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Recognition Model ◽  
Model Based ◽  
Fault Recognition ◽  
Transformer Core ◽  
Identification Model

Abstract In order to realize transformer voiceprint recognition, a transformer voiceprint recognition model based on Mel spectrum convolution neural network is proposed. Firstly, the transformer core looseness fault is simulated by setting different preloads, and the sound signals under different preloads are collected; Secondly, the sound signal is converted into a spectrogram that can be trained by convolutional neural network, and then the dimension is reduced by Mel filter bank to draw Mel spectrogram, which can generate spectrogram data sets under different preloads in batch; Finally, the data set is introduced into convolutional neural network for training, and the transformer voiceprint fault recognition model is obtained. The results show that the training accuracy of the proposed Mel spectrum convolution neural network transformer identification model is 99.91%, which can well identify the core loosening faults.

Vision-based automated crack detection using convolutional neural networks for condition assessment of infrastructure

2020 ◽  
pp. 147592172096544
Author(s):  
Aravinda S Rao ◽  
Tuan Nguyen ◽  
Marimuthu Palaniswami ◽  
Tuan Ngo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Concrete Structures ◽  
Area Under The Curve ◽  
Network Models ◽  
Condition Assessment ◽  
Neural Network Models ◽  
Data Set ◽  
Inspection Method

With the growing number of aging infrastructure across the world, there is a high demand for a more effective inspection method to assess its conditions. Routine assessment of structural conditions is a necessity to ensure the safety and operation of critical infrastructure. However, the current practice to detect structural damages, such as cracks, depends on human visual observation methods, which are prone to efficiency, cost, and safety concerns. In this article, we present an automated detection method, which is based on convolutional neural network models and a non-overlapping window-based approach, to detect crack/non-crack conditions of concrete structures from images. To this end, we construct a data set of crack/non-crack concrete structures, comprising 32,704 training patches, 2074 validation patches, and 6032 test patches. We evaluate the performance of our approach using 15 state-of-the-art convolutional neural network models in terms of number of parameters required to train the models, area under the curve, and inference time. Our approach provides over 95% accuracy and over 87% precision in detecting the cracks for most of the convolutional neural network models. We also show that our approach outperforms existing models in literature in terms of accuracy and inference time. The best performance in terms of area under the curve was achieved by visual geometry group-16 model (area under the curve = 0.9805) and best inference time was provided by AlexNet (0.32 s per image in size of 256 × 256 × 3). Our evaluation shows that deeper convolutional neural network models have higher detection accuracies; however, they also require more parameters and have higher inference time. We believe that this study would act as a benchmark for real-time, automated crack detection for condition assessment of infrastructure.

scholarly journals Skin Cancer Detection using CNN Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 45-49
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Well Being ◽  
Training Data ◽  
Data Sets ◽  
Data Set ◽  
Sequential Model ◽  
Pigmented Lesions ◽  
Testing Data

The project “Disease Prediction Model” focuses on predicting the type of skin cancer. It deals with constructing a Convolutional Neural Network(CNN) sequential model in order to find the type of a skin cancer which takes a huge troll on mankind well-being. Since development of programmed methods increases the accuracy at high scale for identifying the type of skin cancer, we use Convolutional Neural Network, CNN algorithm in order to build our model . For this we make use of a sequential model. The data set that we have considered for this project is collected from NCBI, which is well known as HAM10000 dataset, it consists of massive amounts of information regarding several dermatoscopic images of most trivial pigmented lesions of skin which are collected from different sufferers. Once the dataset is collected, cleaned, it is split into training and testing data sets. We used CNN to build our model and using the training data we trained the model , later using the testing data we tested the model. Once the model is implemented over the testing data, plots are made in order to analyze the relation between the echos and loss function. It is also used to analyse accuracy and echos for both training and testing data.

scholarly journals Steel Defects Analysis Using CNN (Convolutional Neural Networks)

2020 ◽  
Vol 23 (6) ◽  
pp. 1155-1171
Author(s):  
Rodion Dmitrievich Gaskarov ◽  
Alexey Mikhailovich Biryukov ◽  
Alexey Fedorovich Nikonov ◽  
Daniil Vladislavovich Agniashvili ◽  
Danil Aydarovich Khayrislamov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Data ◽  
Training Image ◽  
Data Set ◽  
Image Encoding ◽  
Almost Everywhere ◽  
Defects Analysis ◽  
Hot Rolled ◽  
Hot Rolled Steel

Steel is one of the most important bulk materials these days. It is used almost everywhere - from medicine to industry. Detecting this material's defects is one of the most challenging problems for industries worldwide. This process is also manual and time-consuming. Through this study we tried to automate this process. A convolutional neural network model UNet was used for this task for more accurate segmentation with less training image data set for our model. The essence of this NN (neural network) is in step-by-step convolution of every image (encoding) and then stretching them to initial resolution, consequently getting a mask of an image with various classes on it. The foremost modification is changing an input image's size to 128x800 px resolution (original images in dataset are 256x1600 px) because of GPU memory size's limitation. Secondly, we used ResNet34 CNN (convolutional neural network) as encoder, which was pre-trained on ImageNet1000 dataset with modified output layer - it shows 4 layers instead of 34. After running tests of this model, we obtained 92.7% accuracy using images of hot-rolled steel sheets.

Performance of a convolutional neural network algorithm for tooth detection and numbering on periapical radiographs

2021 ◽  
Author(s):  
Cansu Görürgöz ◽  
Kaan Orhan ◽  
Ibrahim Sevki Bayrakdar ◽  
Özer Çelik ◽  
Elif Bilgir ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
True Positive Rate ◽  
Turnaround Time ◽  
Classification Model ◽  
Data Sets ◽  
Data Set ◽  
Region Detection ◽  
Positive Rate

Objectives: The present study aimed to evaluate the performance of a Faster Region-based Convolutional Neural Network (R-CNN) algorithm for tooth detection and numbering on periapical images. Methods: The data sets of 1686 randomly selected periapical radiographs of patients were collected retrospectively. A pre-trained model (GoogLeNet Inception v3 CNN) was employed for pre-processing, and transfer learning techniques were applied for data set training. The algorithm consisted of: (1) the Jaw classification model, (2) Region detection models, and (3) the Final algorithm using all models. Finally, an analysis of the latest model has been integrated alongside the others. The sensitivity, precision, true-positive rate, and false-positive/negative rate were computed to analyze the performance of the algorithm using a confusion matrix. Results: An artificial intelligence algorithm (CranioCatch, Eskisehir-Turkey) was designed based on R-CNN inception architecture to automatically detect and number the teeth on periapical images. Of 864 teeth in 156 periapical radiographs, 668 were correctly numbered in the test data set. The F1 score, precision, and sensitivity were 0.8720, 0.7812, and 0.9867, respectively. Conclusion: The study demonstrated the potential accuracy and efficiency of the CNN algorithm for detecting and numbering teeth. The deep learning-based methods can help clinicians reduce workloads, improve dental records, and reduce turnaround time for urgent cases. This architecture might also contribute to forensic science.

scholarly journals Computer Aided Detection of Polyps in Whitelight- Colonoscopy Images using Deep Neural Networks

2019 ◽  
Vol 5 (1) ◽  
pp. 231-234 ◽  
Author(s):  
Thomas Wittenberg ◽  
Pascal Zobel ◽  
Magnus Rathke ◽  
Steffen Mühldorfer
Keyword(s):  
Neural Network ◽  
Image Data ◽  
Public Image ◽  
Computer Assisted ◽  
Data Sets ◽  
Polyp Detection ◽  
Data Set ◽  
Screening Programs ◽  
Computer Assisted Detection ◽  
Examination Process

AbstractEarly detection of polyps is one central goal of colonoscopic screening programs. To support gastroenterologists during this examination process, deep convolutional neural network can be applied for computer-assisted detection of neoplastic lesions. In this work, a Mask R-CNN architecture was applied. For training and testing, three independent colonoscopy data sets were used, including 2484 HD labelled images with polyps from our clinic, as well as two public image data sets from the MICCAI 2015 polyp detection challenge, consisting of 612 SD and 194 HD labelled images with polyps. After training the deep neural network, best results for the three test data sets were achieved in the range of recall = 0.92, precision = 0.86, F1 = 0.89 (data set A), rec = 0.86, prec = 0.80, F1 = 0.82 (data set B) and rec = 0.83, prec = 0.74, F1 = 0.79 (data set C).

scholarly journals Application of a convolutional neural network for seismic phase picking of mining-induced seismicity

2020 ◽  
Vol 224 (1) ◽  
pp. 230-240
Author(s):  
Sean W Johnson ◽  
Derrick J A Chambers ◽  
Michael S Boltz ◽  
Keith D Koper
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Induced Seismicity ◽  
Southern California ◽  
High Stress ◽  
Training Data ◽  
Data Sets ◽  
Residual Variance ◽  
Data Set ◽  
Mining Induced Seismicity

SUMMARY Monitoring mining-induced seismicity (MIS) can help engineers understand the rock mass response to resource extraction. With a thorough understanding of ongoing geomechanical processes, engineers can operate mines, especially those mines with the propensity for rockbursting, more safely and efficiently. Unfortunately, processing MIS data usually requires significant effort from human analysts, which can result in substantial costs and time commitments. The problem is exacerbated for operations that produce copious amounts of MIS, such as mines with high-stress and/or extraction ratios. Recently, deep learning methods have shown the ability to significantly improve the quality of automated arrival-time picking on earthquake data recorded by regional seismic networks. However, relatively little has been published on applying these techniques to MIS. In this study, we compare the performance of a convolutional neural network (CNN) originally trained to pick arrival times on the Southern California Seismic Network (SCSN) to that of human analysts on coal-mine-related MIS. We perform comparisons on several coal-related MIS data sets recorded at various network scales, sampling rates and mines. We find that the Southern-California-trained CNN does not perform well on any of our data sets without retraining. However, applying the concept of transfer learning, we retrain the SCSN model with relatively little MIS data after which the CNN performs nearly as well as a human analyst. When retrained with data from a single analyst, the analyst-CNN pick time residual variance is lower than the variance observed between human analysts. We also compare the retrained CNN to a simpler, optimized picking algorithm, which falls short of the CNN's performance. We conclude that CNNs can achieve a significant improvement in automated phase picking although some data set-specific training will usually be required. Moreover, initializing training with weights found from other, even very different, data sets can greatly reduce the amount of training data required to achieve a given performance threshold.

scholarly journals Prediction of Protein–Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets

2020 ◽  
Vol 21 (2) ◽  
pp. 467 ◽  
Author(s):  
Zengyan Xie ◽  
Xiaoya Deng ◽  
Kunxian Shu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Protein Interaction ◽  
Protein Complexes ◽  
Area Under The Curve ◽  
Data Sets ◽  
Data Set ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Remarkable Result

Protein–protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the development of different kinds of prediction algorithms. We propose a convolutional neural network for PPI site prediction and use residue binding propensity to improve the positive samples. Our method obtains a remarkable result of the area under the curve (AUC) = 0.912 on the improved data set. In addition, it yields much better results on samples with high binding propensity than on randomly selected samples. This suggests that there are considerable false-positive PPI sites in the positive samples defined by the distance between residue atoms.

scholarly journals Diagnostic Breast Cancer Image Data Classification using CNN

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
K. Karthik, Et. al.
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Image Data ◽  
Computer Assisted ◽  
Data Sets ◽  
Computer Assisted Detection ◽  
Deep Cnn ◽  
Image Data Classification

Breast cancer has been  dangerous form of cancer. In this report, we use a convolutional neural network to scan and separate infected cells.In this we diagnose if its benign or malignant cancer bulk using computer assisted detection(CAD). The productivity of open CAD has always been inadequate. Here, we use a deep CNN-based content detection method.We create narrower and broader images of histology patches with cell and tumour attributes. CNN constitutes unorganized data specifically for image data which has been said to be thriving in the area of image recognition .We use highly interconnected layer first cnn, in which those layers are incorporated before the first convolutional layer, since CNN does not support data sets.  

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