scholarly journals Simulation and Recognition of Concrete Lining Infiltration Degree via an Indoor Experiment

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Dongsheng Wang ◽  
Jun Feng ◽  
Xinpeng Zhao ◽  
Yeping Bai ◽  
Yujie Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cement Mortar ◽  
Recognition Accuracy ◽  
Image Data ◽  
Concrete Lining ◽  
Learning Method ◽  
Recognition Method ◽  
Data Set ◽  
Recognition Model ◽  
High Recognition Accuracy

It is difficult to form a method for recognizing the degree of infiltration of a tunnel lining. To solve this problem, we propose a recognition method by using a deep convolutional neural network. We carry out laboratory tests, prepare cement mortar specimens with different saturation levels, simulate different degrees of infiltration of tunnel concrete linings, and establish an infrared thermal image data set with different degrees of infiltration. Then, based on a deep learning method, the data set is trained using the Faster R-CNN+ResNet101 network, and a recognition model is established. The experiments show that the recognition model established by the deep learning method can be used to select cement mortar specimens with different degrees of infiltration by using an accurately minimized rectangular outer frame. This model shows that the classification recognition model for tunnel concrete lining infiltration established by the indoor experimental method has high recognition accuracy.

scholarly journals Segmentation of Polyp Instruments using UNet based deep learning model

2021 ◽  
Vol 1 (1) ◽  
pp. 44-46
Author(s):  
Ashar Mirza ◽  
Rishav Kumar Rajak
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Learning Model ◽  
Learning Method ◽  
Data Set ◽  
Test Dataset ◽  
Segmentation Task ◽  
Deep Learning Model

In this paper, we present a UNet architecture-based deep learning method that is used to segment polyp and instruments from the image data set provided in the MedAI Challenge2021. For the polyp segmentation task, we developed a UNet based algorithm for segmenting polyps in images taken from endoscopies. The main focus of this task is to achieve high segmentation metrics on the supplied test dataset. Similarly for the polyp segmentation task, in the instrument segmentation task, we have developed UNet based algorithms for segmenting instruments present in colonoscopy videos.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

A Proposed Speaker Recognition Method B Based on Long-Term Voice Features and Fuzzy Logic

2021 ◽  
Vol 39 (1B) ◽  
pp. 1-10
Author(s):  
Iman H. Hadi ◽  
Alia K. Abdul-Hassan
Keyword(s):  
Fuzzy Logic ◽  
Speaker Recognition ◽  
Recognition Accuracy ◽  
Inner Product ◽  
Maximum Frequency ◽  
Recognition Method ◽  
Data Set ◽  
Zero Crossing ◽  
Zero Crossing Rate

Speaker recognition depends on specific predefined steps. The most important steps are feature extraction and features matching. In addition, the category of the speaker voice features has an impact on the recognition process. The proposed speaker recognition makes use of biometric (voice) attributes to recognize the identity of the speaker. The long-term features were used such that maximum frequency, pitch and zero crossing rate (ZCR).  In features matching step, the fuzzy inner product was used between feature vectors to compute the matching value between a claimed speaker voice utterance and test voice utterances. The experiments implemented using (ELSDSR) data set. These experiments showed that the recognition accuracy is 100% when using text dependent speaker recognition.

scholarly journals Using Deep Learning for Image-Based Potato Tuber Disease Detection

2019 ◽  
Vol 109 (6) ◽  
pp. 1083-1087 ◽  
Author(s):  
Dor Oppenheim ◽  
Guy Shani ◽  
Orly Erlich ◽  
Leah Tsror
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Image Data ◽  
Object Identification ◽  
Plant Diseases ◽  
Disease Classification ◽  
Successful Implementation ◽  
Potato Tubers ◽  
Data Set ◽  
Convolutional Networks

Many plant diseases have distinct visual symptoms, which can be used to identify and classify them correctly. This article presents a potato disease classification algorithm that leverages these distinct appearances and advances in computer vision made possible by deep learning. The algorithm uses a deep convolutional neural network, training it to classify the tubers into five classes: namely, four disease classes and a healthy potato class. The database of images used in this study, containing potato tubers of different cultivars, sizes, and diseases, was acquired, classified, and labeled manually by experts. The models were trained over different train-test splits to better understand the amount of image data needed to apply deep learning for such classification tasks. The models were tested over a data set of images taken using standard low-cost RGB (red, green, and blue) sensors and were tagged by experts, demonstrating high classification accuracy. This is the first article to report the successful implementation of deep convolutional networks, popular in object identification, to the task of disease identification in potato tubers, showing the potential of deep learning techniques in agricultural tasks.

scholarly journals A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guangpeng Fan ◽  
Feixiang Chen ◽  
Danyu Chen ◽  
Yan Li ◽  
Yanqi Dong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Test Data ◽  
Smartphone Application ◽  
Geological Survey ◽  
Data Set ◽  
Recognition Model ◽  
Communication Device ◽  
Rock Lithology

In the geological survey, the recognition and classification of rock lithology are an important content. The recognition method based on rock thin section leads to long recognition period and high recognition cost, and the recognition accuracy cannot be guaranteed. Moreover, the above method cannot provide an effective solution in the field. As a communication device with multiple sensors, smartphones are carried by most geological survey workers. In this paper, a smartphone application based on the convolutional neural network is developed. In this application, the phone’s camera can be used to take photos of rocks. And the types and lithology of rocks can be quickly and accurately identified in a very short time. This paper proposed a method for quickly and accurately recognizing rock lithology in the field. Based on ShuffleNet, a lightweight convolutional neural network used in deep learning, combined with the transfer learning method, the recognition model of the rock image was established. The trained model was then deployed to the smartphone. A smartphone application for identifying rock lithology was designed and developed to verify its usability and accuracy. The research results showed that the accuracy of the recognition model in this paper was 97.65% on the verification data set of the PC. The accuracy of recognition on the test data set of the smartphone was 95.30%, among which the average recognition time of the single sheet was 786 milliseconds, the maximum value was 1,045 milliseconds, and the minimum value was 452 milliseconds. And the single-image accuracy above 96% accounted for 95% of the test data set. This paper presented a new solution for the rapid and accurate recognition of rock lithology in field geological surveys, which met the needs of geological survey personnel to quickly and accurately identify rock lithology in field operations.

A Deep Learning Method for Limited-View Intravascular Photoacoustic Image Reconstruction

2020 ◽  
Vol 10 (11) ◽  
pp. 2707-2713
Author(s):  
Zheng Sun ◽  
Xiangyang Yan
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Imaging Modality ◽  
Image Data ◽  
Structural Similarity ◽  
Simulated Image ◽  
Cross Sectional ◽  
Data Set ◽  
Reconstruction Methods ◽  
Under Sampling

Intravascular photoacoustic tomography (IVPAT) is a newly developed imaging modality in the interventional diagnosis and treatment of coronary artery diseases. Incomplete acoustic measurement caused by limitedview scanning of the detector in the vascular lumen results in under-sampling artifacts and distortion in the images reconstructed by using the standard reconstruction methods. A method for limited-view IVPAT image reconstruction based on deep learning is presented in this paper. A convolutional neural network (CNN) is constructed and trained with computer-simulated image data set. Then, the trained CNN is used to optimize the cross-sectional images of the vessel which are recovered from the incomplete photoacoustic measurements by using the standard time-reversal (TR) algorithm to obtain the images with the improved quality. Results of numerical demonstration indicate that the method can effectively reduce the image distortion and artifacts caused by the limited-view detection. Furthermore, it is superior to the compressed sensing (CS) method in recovering the unmeasured information of the imaging target with the structural similarity around 10% higher than CS reconstruction.

scholarly journals Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liu Yan ◽  
Sun Xin
Keyword(s):  
Machine Vision ◽  
Camera Calibration ◽  
Recognition Accuracy ◽  
Calibration Method ◽  
Recognition Method ◽  
Systematic Analysis ◽  
High Recognition Accuracy ◽  
Intelligent Recognition ◽  
Region Growth ◽  
Design Experiments

In view of the intelligent demand of tennis line examination, this paper performs a systematic analysis on the intelligent recognition of tennis line examination. Then, a tennis line recognition method based on machine vision is proposed. In this paper, the color region of the image recognition region is divided based on the region growth, and the rough estimation of the court boundary is realized. In order to achieve the effect of camera calibration, a fast camera calibration method which can be used for a variety of court types is proposed. On the basis of camera calibration, a tennis line examination and segmentation system based on machine vision analysis is constructed, and the experimental results are verified by design experiments. The results show that the machine vision analysis-based intelligent segmentation system of tennis line examination has high recognition accuracy and can meet the actual needs of tennis line examination.

scholarly journals Recognition Method of Tunnel Lining Defects Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Anfu Zhu ◽  
Shuaihao Chen ◽  
Fangfang Lu ◽  
Congxiao Ma ◽  
Fengrui Zhang
Keyword(s):  
Deep Learning ◽  
Tunnel Lining ◽  
Training Algorithms ◽  
Test Results ◽  
Recognition Method ◽  
Training Time ◽  
Recognition Model ◽  
New Ideas ◽  
Defect Recognition ◽  
Ground Penetrating

The defect identification of tunnel lining is a task with a lot of tasks and time-consuming work, and currently, it mainly relies on manual operation. This paper takes the ground-penetrating radar image of the internal defects of the lining as the research object, and chooses the popular VGG16, ResNet34 convolutional neural network (CNN) to build the automatic recognition model for comparative study, and proposes an improved ResNet34 defect-recognition model. In this paper, SGD and Adam training algorithms are used to update network parameters, and the PyTorch depth framework is used to train the network. The test results show that the ResNet34 network has faster convergence speed, higher accuracy rate, and shorter training time than the VGG16 network. The ResNet34 network using the Adam algorithm can achieve 99.08% accuracy. The improved ResNet34 network can achieve an accuracy of 99.25%, and at the same, reduce the parameter amount by 4.22% compared with the ResNet34 network, which can better identify defects in the lining. The research in this paper shows that the deep learning method can provide new ideas for the identification of tunnel lining defects.

scholarly journals Research on Recognition Method of COVID-19 Images Based on Deep Learning

2020 ◽  
Author(s):  
dongshen ji ◽  
yanzhong zhao ◽  
zhujun zhang ◽  
qianchuan zhao
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Small Sample ◽  
Experimental Results ◽  
Ct Image ◽  
Recognition Method ◽  
Sample Recognition

In view of the large demand for new coronary pneumonia covid19 image recognition samples,the recognition accuracy is not ideal.In this paper,a new coronary pneumonia positive image recognition method proposed based on small sample recognition. First, the CT image pictures are preprocessed, and the pictures are converted into the picture formats which are required for transfer learning. Secondly, perform small-sample image enhancement and expansion on the converted picture, such as miscut transformation, random rotation and translation, etc.. Then, multiple migration models are used to extract features and then perform feature fusion. Finally,the model is adjusted by fine-tuning.Then train the model to obtain experimental results. The experimental results show that our method has excellent recognition performance in the recognition of new coronary pneumonia images,even with only a small number of CT image samples.

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