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Research on reconstruction of Pipeline Defects Under Coatings Based on Pulsed Eddy Current and Improved DCGAN

Research on Reconstruction of Pipeline Defects Under Coating...

引用

Algorithm, image Processing and Machine Vision (AIPMV), International conference on

作者： Song Huang Longyi Zhang Lin Yang Dingding Fan Qin Cai Chongqing Special Equipment Inspection and Research Institute Chongqing China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

The detection of surface defects in pipelines with coating layers has long been a significant challenge in the industrial field of surface defect detection. Pulsed Eddy Current Testing (PECT) can be employed to obtain approximate defect images of suspected defect areas through matrix measurements outside the coating layer. However, due to a number of variables, including lift-off height, excitation square wave parameters, and the properties of the pipeline material, the matrix approximation images obtained differ significantly from the actual defect images. This paper proposes a method based on improved DCGAN to reconstruct defect images. First, photographs are taken of samples without coating layers, and precise defect images are obtained through manual annotation as positive samples. Then, matrix imaging data collected from pipelines with coating layers are used as input samples for generation. Through the improved DCGAN, defect images that closely resemble real defect images can be ultimately generated, thereby achieving the purpose of defect detection. The method proposed in this paper can effectively identify surface defects in pipelines under coating layers.

关键词： Surface reconstruction Surface waves Pipelines Solids Coatings Reliability image reconstruction Surface treatment Defect detection Testing

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Ultra-Low Field Magnetic Resonance image Enhancement based on Deep-Learning Method

Ultra-Low Field Magnetic Resonance Image Enhancement based o...

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Health Big data and Intelligent Healthcare (ICHIH), International conference on

作者： Fanqin Meng Yi Guo Wei He Zheng Xu School of Electrical Engineering Chongqing University Chongqing 400044 China Central Hospital Chongqing University Chongqing 400014 China

In recent years, there has been growing interest in ultra-low-field (ULF) magnetic resonance imaging (MRI) technology aimed at addressing the limitations of high-field MRI systems, including high cost, immobility, and maintenance challenges. Under ultra-low background magnetic field conditions (<100 mT), MRI signals are weaker, and the overall signal-to-noise ratio of the k-space data used for imaging is poor. Additionally, the image resolution is often lower to shorten the scan time. These two factors significantly impact image quality. To address these issues, it is essential to enhance image quality during the image reconstruction and post-processing phases. This paper initially processes the k-space data into a low-rank matrix and performs preliminary noise reduction using the Singular Value Decomposition (SVD) algorithm. After image reconstruction, a deep convolutional denoising network is applied for denoising in the image domain. Finally, the denoised image data undergo super-resolution using bicubic interpolation, resulting in enhanced ULF MRI images. These methods, trained on 1.5T high-field MRI image data, are applied to 54 mT ULF MRI brain imaging. The processed image quality is significantly superior to the original images. This advancement is expected to enhance the value and significance of ULF MRI in clinical applications and promote the expansion of MRI technology.

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Application of Satellite image in Disaster Detection 2

Application of Satellite Image in Disaster Detection

引用

2nd International conference on Big data and Artificial Intelligence and Software Engineering (ICBASE)

作者： Jiang, Yufan Cent Univ Econ & Finance China Econ & Management Beijing Peoples R China

ISBN: (纸本)9781665427098

Natural disasters may cause great loss to people's properties and lives. Disaster detection is vital to rescue and reconstruction after natural disasters. In recent years, satellite images are widely used in disaster detection. In this paper, the application of satellite images in disaster detection is reviewed. The development is introduced with two aspects: the improvement of satellite image quality and the development of the processing and detection algorithms. The paper classifies the disaster detection methods into four categories based on the degree of intelligence. The four categories are rule-based methods, color distribution-based methods, multi-object detection methods, and end-to-end detection methods. Methods of each category and their application are described in detail. Related works and state-of-the-arts (SOTAs) are introduced. This paper also evaluates these methods with several aspects.

关键词： component disaster detection satellite image rule-based CNN object-detection

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Prediction of Sexually Transmitted Diseases Using Deep Convolutional Neural Networks for image data 2nd

Prediction of Sexually Transmitted Diseases Using Deep Convo...

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2nd International conference on Forthcoming Networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Mahameed, Ans Ibrahim Mahmood, Rafah Kareem Univ Tikrit Coll Educ Pure Sci Dept Math Tikrit Iraq Univ Technol Baghdad Coll Engn Electromech Engn Dept Baghdad Iraq

ISBN: (纸本)9783031628702;9783031628719

This study uses deep convolutional neural networks to build an STD prediction decision assistance system. Since conventional STD prevention strategies have failed in poor nations, experts have examined social media-based disease control. Deep convolutional neural networks cannot successfully represent data due to their short duration, excessive noise, and informality. Disease tweets were categorized using character-level word vectors from deep learning to develop an epidemic prediction model. Our prediction algorithm missed formal events but notified us 14 days ahead. Our Deep Convolutional Neural Network (DCNN) technology beats cutting-edge methods for this challenge. Improved procedure efficiency and accuracy have been achieved through innovation. Research using convolutional neural networks improved HIV/AIDS detection and categorization. Additionally, an STD prognostic model was developed. The collection comprises benign and malignant STI patient data. Training comprises 80% of the dataset, and testing and validation comprise 20%. 10-fold cross-validation verifies the data. To test the approaches, we gathered 10 IDs with a low-resolution depth camera. They trained and tested on the dataset. Anaconda was the creator of Python algorithms. The basic algorithm of DCNN fails to manage noise and uneven light, creating a worthless output. Graphic representations of foreign script characters can help teach a new language and overcome initial obstacles. Preprocessing lowers noise and low-light issues. Our hypothesis predicted HIV and STDs with 95.47% accuracy, surpassing all previous hypotheses. Nobody equaled our success.

关键词： Hyperspectral imagery Biological data De-striping DCNN Hyperion data

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Fine grained image classification combining dual attention and kernelized bilinear CNN 2

Fine grained image classification combining dual attention a...

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2nd International conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Yang, Jenpeng Liu, Peng School of Electronic and Information Engineering Changchun Unicersity of Science and Technology Jilin Changchun130022 China

ISBN: (纸本)9798350334784

In order to better capture the micro-differences of fine-grained images, this paper suggest an reformative Bilinear CNN method. Firstly, ResNet convolution with tight structure is selected as the feature extraction module. Then, the dual attention model of the local area is built from two dimensions of channel and space. The channel is weighted in channel dimension and the location is weighted in channel dimension. The attention feature graph matrices of the two mechanisms are added together and then accumulated. Finally, sigmoid kernel function is used to nucleate the outer product matrix and model the nonlinear relationship between channels. The method was tested on fine-grained data sets. The experimental results show that the proposed method is superior to the similar methods on the three data sets. © 2022 IEEE.

关键词： Matrix algebra

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Smart Seed Classification System based on MobileNetV2 Architecture 2

Smart Seed Classification System based on MobileNetV2 Archit...

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2nd International conference on Computing and Information Technology, ICCIT 2022

作者： Hamid, Yasir Wani, Sharyar Soomro, Arjumand Bano Alwan, Ali A. Gulzar, Yonis Abu Dhabi Polytechnic Information Security and Engineering Technology Abudhabi United Arab Emirates International Islamic University Malaysia Kulliyyah of Information and Communication Technology Department of Computer Science Gombak Malaysia College of Business Administration King Faisal University Department of Management Information Systems Al-Ahsa Saudi Arabia School of Theoretical and Applied Science Ramapo College of New Jersey MahwahNJ United States College of Business Administration King Faisal University Department of Management Information Systems Al-Ahsa Saudi Arabia

ISBN: (纸本)9781665436052

The agricultural transformation in the last decade using artificial intelligence has led to significant gains in productivity and profitability. The traditional machine learning approaches present inherent limitations in extracting features and information from image data. Deep learning techniques, particularly CNN's, help to overcome these limitations due to their multi-level architecture. Various deep learning applications in agriculture include crop disease identification, fruit classification, and germination rate monitoring. Seed image analysis is considered a significant task for the preservation of biodiversity and sustainability. This research uses MobileNetV2, a deep learning convolutional neural network (DCNNs) for seed classification. This model has been preferred due to its simple architecture and memory-efficient characteristics. A total of 14 different classes of seeds were used for the experimentation. The results indicate accuracies of 98% and 95% on training and test sets, respectively. © 2022 IEEE.

关键词： Seed

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Creation and Classification of Kannada Meme dataset: Exploring Domain and Troll Categories 2nd

Creation and Classification of Kannada Meme Dataset: Explo...

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2nd International conference on Speech and Language Technologies for Low-Resource Languages, SPELLL 2023

作者： Kundargi, Sahana Y. N, Lohith M, Anand Kumar Chakravarthi, Bharathi Raja Department of Information Technology National Institute of Technology Karnataka Surathkal India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Galway Ireland

ISBN: (纸本)9783031584947

In this pioneering research, the first-ever Kannada memes dataset is established, marking a groundbreaking contribution. This dataset encompasses 2002 memes, spanning various categories such as movies, politics, sports, trolls, and non-troll memes. The classification models have been meticulously fine-tuned for memes, incorporating image-based models using DenseNet169 and text-based models with BERT for text encoding. An innovative multimodal approach combines insights from images and text, acknowledging the comprehensive nature of meme content. Throughout the study, model strengths and weaknesses are assessed, emphasizing their reliance on cutting-edge technologies like Deep Learning and Natural Language Processing. Valuable improvements are recommended, such as the implementation of oversampling techniques and regular dataset updates to enhance relevance and accuracy. This work extends beyond immediate research, contributing to the development of adaptive meme classification systems, particularly for Kannada-speaking audiences within the evolving meme culture landscape. Notably, the results indicate that multimodal models achieved the best scores for domain classification, while image-based models excelled in troll meme classification, further highlighting the significance of this approach within the field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Classification (of information)

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Robust Self-supervised Correspondence Learning for SAR-optical image Matching 2

Robust Self-supervised Correspondence Learning for SAR-optic...

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2nd International conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2022

作者： An, Chengjin Cao, Fanzhi Wang, Pu Shi, Tianxin School of Electronic Science National University of Defense Technology ChangSha China

ISBN: (数字)9781665476164

ISBN: (纸本)9781665476164

SAR-optical image matching is a prerequisite for the joint use of these two modalities in many remote sensing applications. Due to significant nonlinear radiometric difference, it is a challenging task to perform robust matching between SAR and optical images. Recently, supervised deep learning methods have become the mainstream in the task of image feature matching, demonstrating the great power in feature description. One drawback of supervised deep learning methods is the huge demand for a vast amount of labelled training data, causing a big obstacle to its application in remote sensing. For this problem, we propose a self-supervised deep learning method to find correspondences using local image structure feature for SAR-optical image matching. In order to learn to exploit image structure cues, our network operates on the gradient of the input image instead of raw image intensity to produce dense descriptors, which paves the way for the self-supervised training. Next, a 4-D correlation map of SAR-optical images is computed using these descriptors. Lastly, reliable point-to-point correspondences can be extracted from the correlation map by means of the mutual nearest neighbor (MNN) and the RANSAC-based outlier removal. Extensive experimental results demonstrate that our method can establish robust correspondences between SAR and optical images with no demand for labelled data. © 2022 IEEE.

关键词： Synthetic aperture radar

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An incomplete License Plate image Intelligent Recognition System Based on the Generated Counter Network 2nd

An Incomplete License Plate Image Intelligent Recognition Sy...

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2nd International conference on IoT and Big data Technologies for HealthCare, IoTCare 2021

作者： Meng, Mi He, Chun-hu Qi, Xiao-jing Chongqing Telecommunication Vocational College Chongqing402274 China Chongqing Toramon Technology Co. Ltd. Chongqing401121 China

ISBN: (纸本)9783030941840

In order to solve the problems of high recognition accuracy and long time-consuming recognition in the traditional incomplete license plate image recognition system, the paper proposes an incomplete license plate image intelligent recognition system based on a generative confrontation network. According to the composition of the recognition system, the framework of the intelligent recognition system for incomplete license plate images is designed. The hardware platform of the system is the S3C6410 embedded development board, which is based on Samsung’s ARM11 processor chip, and the CPU is based on the ARM111176JZF-S core design. A powerful multimedia processing unit is integrated inside, which supports hardware encoding and decoding of video files in formats such as Mpeg4, H.264/H.263, and can be output to LCD and TV display at the same time. The 6410 uses a high-density 6-layer board design, which integrates 256M DDR RAM, 256M/1 GB SLC Nand Flash memory. The system software uses a generative confrontation network to repair the incomplete license plate image, and combines the template matching method to complete the license plate image recognition. Experimental results show that the designed intelligent recognition system has high recognition accuracy and can effectively reduce the time-consuming recognition. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Liquid crystal displays

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Super-resolution reconstruction of infrared images based on convolutional neural network

Super-resolution reconstruction of infrared images based on ...

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Computer Network Technology and Electronic and Information Engineering (CNTEIE), International conference on

作者： Xiaolong Ai Wei Yang School of Aeronautics and Astronautics Xihua University Chengdu China

ISBN: (数字)9798331524432

ISBN: (纸本)9798331524449

Due to the technical limitations of devices and processes, the resolution of infrared images is relatively low compared with that of visible light images, and there are shortcomings such as blurred detailed texture features. In this paper, a super-resolution method for infrared images is proposed based on convolutional neural network. The method consists of a two-branch infrared image information extraction module by combining dense connections and the multi-scale attention residual block proposed in this paper, and the module is used to construct an infrared image super-resolution network. Experimental results show that the infrared images reconstructed by this paper's method outperform the SRCNN, EDSR, RDN, and MFDRN super-resolution reconstruction models based on deep learning in terms of peak signal-to-noise ratio. The average PSNR of the reconstructed images is improved by 2.27 dB and 2.14 dB over the traditional Bicubic interpolation method at scale factors of x2 and x4, respectively.

关键词： Interpolation PSNR Computational modeling Superresolution Feature extraction Visual effects Information retrieval Convolutional neural networks data mining image reconstruction

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