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Research and Implementation Of image Recognition of Tea Based on Deep Learning 21

Research and Implementation Of Image Recognition of Tea Base...

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21st ACIS International Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Winter)

作者： Gao, Mucong Shi, Minyong Li, Chunfang Commun Univ China Sch Comp Sci & Cybersecur Beijing Peoples R China

ISBN: (纸本)9781665418430

With the advent of the Internet era, deep learning technology is developing rapidly in a wide range of application scenarios. As one of the main research methods of deep learning, convolutional neural network is widely used in the field of image recognition[5]. This paper hopes to build a modern intelligent tea image recognition system by combining in-depth learning technology, so as to help inherit traditional Chinese culture. The main work of this paper is as follows: (1) Making a sample data set containing about 49000 pictures;(2) Constructing convolution neural network;(3) The correct rate of training image recognition model is 96%;(4) Realize tea picture recognition;(5) Build the web page. Under the framework of tensorflow, the program of this subject realizes the tea picture recognition function by training convolution neural network[5][6], and builds it in the beautiful web page, so that the tea culture can be presented and spread in a more modern form, which is conducive to the inheritance and development of Chinese traditional culture.

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Brand Logo image Classification Based on Deep Convolutional Generative Adversarial Network

Brand Logo Image Classification Based on Deep Convolutional ...

引用

distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： Yang Wang Guangzhou Institute of Science Technology Guangzhou China

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Brand logo image examines how a critical dimension of logo design, namely the naturalness of the logo color, influences brand design induced by the logo. However, high complexity is observed, requiring higher quality data and susceptibility to overfitting when there is insufficient training data. The implementation of the proposed system involved the utilization of a Deep Convolutional Generative Adversarial Network (DCGAN) reduce overfitting and efficiently handle noise and distortion in the logo image, which is essential in brand logo image processing. The brand logo image dataset forwarded to the preprocessing stage included pixel values, which may lead to the loss of contrast, shadow, sharpness, noise removal, and structure in some relevant images. The classification involved DCGAN, aiming to achieve high-quality logo image prediction, noise reduction, and handling of overfitting by incorporating synthetic logo images. The proposed DCGAN method achieves promising performance, including a mean Average Precision (mAP) of 52.25, Accuracy of 97.09%, Precision of 97.01 %, Recall of 98.10%, and F1-score of 97.11 %. This performance is compared to existing methods such as YOLOv3 and Convolutional Neural Network (CNN) - Long Short-Term Memory (LSTM).

关键词： YOLO image color analysis Noise reduction Training data Generative adversarial networks Data models Convolutional neural networks

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Construction of Three-dimensional Real-time Rendering Model of Power Grid Based on image processing Algorithm

Construction of Three-dimensional Real-time Rendering Model ...

引用

Internet of Things, Robotics and distributed Computing (ICIRDC), International conference on

作者： Xiao Han Xuefeng Shao Lele Xu Weitao Wang Mengdi Yu Ningbo Electric Power Design Institute Co. Ltd. Ningbo China

image processing algorithm has important application value in 3D real-time rendering of power grid. By reasonable selection and application of image processing algorithm, the effect and performance of 3D visualization of power grid can be improved. Through the research on related issues, this paper aims to develop and evaluate a 3D real-time rendering model of power grid to meet the needs of power system in visualization, interaction and analysis. In order to achieve this goal, this paper adopts a set of comprehensive methods and constructs a 3D real-time rendering model with four modules: data input, rendering, interaction and analysis. Among them, the rendering module combines a variety of image processing algorithms to ensure high quality rendering results; The interactive module provides users with an intuitive and easy-to-operate interface. In order to verify the performance of the model, several groups of simulation experiments are further designed, and the whole process and data of the experiments are recorded in detail. The results show that this model has achieved a stable 60 FPS (Frames Per Second) on a high-performance computer, and the image quality has also been highly praised by users. In addition, the average user experience score reached 9.2 points. The model can not only provide powerful visual support for power system, but also be used as an analysis tool to help decision-making and planning.

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A Comprehensive Survey on image Filtering & Inpainting for Improved image Quality

A Comprehensive Survey on Image Filtering & Inpainting for I...

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Information Technology (OCIT), OITS International conference on

作者： Kalpana Patil Varsha Bendre Department of Electronics and Telecommunication Pimpri Chinchwad College of Engineering Pune Pune India

Computer vision has significantly impacted information technology over the last few years. The image processing process lays the groundwork for computer vision with important components, including image filtering and image inpainting. Digital image processing is one of the broadest areas for study. It has several applications in military, medicine, space, automobile, security systems and many others. To increase the effectiveness of the system's noise suppression structural similarity index, universal image quality index, texture detection, mean square error, peak signal-to-noise ratio, and many other metrics. The evolutionary examination of several image-filtering methods is included in this paper. Among these methods, the most significant are median filtering, pixel similarity weighted frame averaging, bilateral filtering, anisotropic diffusion, and Gaussian filtering. This study focuses on an evolutionary analysis of image inpainting methods based on parallel pipelines, convolution neural networks and generative adversarial networks. The analysis of numerous works on improving image quality reveals that the hybrid model of image-filtering and image inpainting for image quality improvement is the promising solution for improved quality of image required for computer vision applications.

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Machine Learning-based Transformer Station Construction Safety Detection System

Machine Learning-based Transformer Station Construction Safe...

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Power, Electrical Engineering, Electronics and Control (PEEEC), International conference on

作者： Dapeng Zhang Li Quan Jinghao He Xiaoli Song Yanling Luo Chengdu Chengdian Electric Power Engineering Design Co. Ltd Chengdu Sichuan China

ISBN: (数字)9798350378917

ISBN: (纸本)9798350378924

The construction safety of transformer stations is an important issue in the power industry, and traditional safety inspection methods suffer from high labor costs and low efficiency. The construction safety detection system for transformer stations based on Convolutional Neural Network (CNN) has attracted widespread attention in recent years, but there is still relatively little comprehensive summary of its research methods and results. This study elaborates on the construction of a CNN-based transformer station construction safety detection system and conducts a deep comparison with traditional SVM models. Through the analysis of research literature, it was found that CNN can effectively distinguish between safe and dangerous behaviors by learning image features and contextual relationships, improving the accuracy of detection. The parallel computing and GPU acceleration capabilities of CNN significantly shorten the detection time and improve the real-time performance of detection.

关键词： Accuracy Weak supervision Computational modeling Graphics processing units parallel processing Transformers Feature extraction Real-time systems Safety Convolutional neural networks

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Design and implementation of big data processing system based on Hadoop 6

Design and implementation of big data processing system base...

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6th International conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Huang, Siqi Xie, Zhenqiang Wang, Jiaxiang Lv, Penghui Wang, Wenrong Guizhou Vocational Technology College of Electronics and Information Kaili556000 China CETC Big Data Research Institute Co. Ltd. Guiyang550022 China

With the increasing application of big data, efficient big data processing systems can improve the utilization of big data and create greater value. Cloud computing technology is a product of massive data, and this article proposes a big data processing system developed based on the Hadoop cloud computing platform. The system adopts technologies such as distributed file system HDFS, distributed parallel database MapReduce, and distributed computing framework Spark. This system can process massive amounts of data without the need for programming, greatly improving data processing efficiency and having broad application prospects. This article analyzes the design and implementation methods of a big data processing system based on Hadoop, and conducts performance tests on it. The test results show that the system has high performance and meets basic big data processing requirements. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Cloud computing

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MCTE: Marrying Convolution and Transformer Efficiently for End-to-End Medical image Segmentation

MCTE: Marrying Convolution and Transformer Efficiently for E...

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IEEE International conference on image processing

作者： Jiuqiang Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China

The purpose of the medical image segmentation task is to delineate different organs or lesion regions in the image, which is an important aid for intelligent clinical medical diagnosis. Recent approaches suffer from the inability to obtain reliable attention, are computationally intensive, and do not exploit the relationships between different samples. We marry convolution and Transformer effectively to establish MCTE for medical image segmentation. The proposed MCTE is an end-to-end network based on U-Net with a parallel learning of three types of attention, namely local attention learning with channel and spatial dimensional convolution, global attention learning with smaller computational effort of swin transformer, and external attention learning with two shared memory storing all medical image information. Extensive experimental results on the ACDC and Synapse dataset, which are widely used for the evaluation of medical image segmentation methods, demonstrate that our proposed method exceeds the compared baseline.

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MSGAT: Multi-Stage Graph Attention Network For Human Motion Prediction

MSGAT: Multi-Stage Graph Attention Network For Human Motion ...

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IEEE International conference on image processing

作者： Ziyang Zheng Ziliang Ren Zhanhao Liang Gulin Wang Qieshi Zhang School of Computer Science and Technology Dongguan University of Technology China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Automatic Control Department Kyrgyz State Technical University named after Iskhak Razzakov Bishkek Kyrgyzstan

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Human motion prediction (HMP) refers to predicting the future body pose from the historical pose sequence. Many existing methods use Graph Convolutional Networks (GCN) to model the human body and convert the human pose from the pose space to the trajectory space or 3D coordinates. Furthermore, GCN treat human poses as a generic graph formed by links between each pair of body joints to encode the dependence of human spatial poses as well as temporal information by working in trajectory space. We design a multi-stage distributed processing network that includes Spatial Dense Graph Convolutional Networks (S-DGCN) and Temporal Dense Graph Convolutional Networks (T-DGCN). The multistage strategy enables us to gradually acquire smoother inputs. Additionally, we have incorporated an attention mechanism within the processing framework, which helps T-DGCN better capture temporal dependencies. As a result, the proposed network not only facilitates more effective feature extraction but also achieves state-of-the-art performance on the CMU-Mocap and 3DPW datasets. Our code is available at https://***/ihavenotgoodname/MSGAT.

关键词： Convolutional codes Solid modeling distributed processing Three-dimensional displays Attention mechanisms Graph convolutional networks Feature extraction

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Efficient distributed image recognition algorithm of deep learning framework TensorFlow

Efficient distributed image recognition algorithm of deep le...

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2021 International conference on Information Technology and Mechanical Engineering, ITME 2021

作者： Xu, Wencai Jiangxi Teachers College Jiangxi Yingtan335000 China

Deep learning requires training on massive data to get the ability to deal with unfamiliar data in the future, but it is not as easy to get a good model from training on massive data. Because of the requirements of deep learning tasks, a deep learning framework has also emerged. This article mainly studies the efficient distributed image recognition algorithm of the deep learning framework TensorFlow. This paper studies the deep learning framework TensorFlow itself and the related theoretical knowledge of its parallel execution, which lays a theoretical foundation for the design and implementation of the TensorFlow distributed parallel optimization algorithm. This paper designs and implements a more efficient TensorFlow distributed parallel algorithm, and designs and implements different optimization algorithms from TensorFlow data parallelism and model parallelism. Through multiple sets of comparative experiments, this paper verifies the effectiveness of the two optimization algorithms implemented in this paper for improving the speed of TensorFlow distributed parallel iteration. The results of research experiments show that the 12 sets of experiments finally achieved a stable model accuracy rate, and the accuracy rate of each set of experiments is above 97%. It can be seen that the distributed algorithm of using a suitable deep learning framework TensorFlow can be implemented in the goal of effectively reducing model training time without reducing the accuracy of the final model. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Iterative methods

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An Anti-forensic Method Based on RS Coding and distributed Storage 21st

An Anti-forensic Method Based on RS Coding and Distributed S...

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21st International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Jiang, Xuhang Wang, Yujue Ding, Yong Liang, Hai Wang, Huiyong Li, Zhenyu Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guangxi Key Lab Cryptog & Informat Secur Guilin Peoples R China Pengcheng Lab Cyberspace Secur Res Ctr Shenzhen Peoples R China Guilin Univ Elect Technol Sch Math & Comp Sci Guilin Peoples R China

ISBN: (纸本)9783030953881;9783030953874

The anti-forensics (AF) technology has become a new field of cybercrime. The problems of existing forensic technologies should be considered from criminals' perspective, so as to make improvement to existing AF technologies. There are two types of AF methods, namely, data hiding and destruction, where most AF tools are primarily based on data hiding. If the data can be intercepted by investigators during the AF process, the remaining data may be destroyed by the criminal, which would make investigators obtain nothing about data information. To address this issue, this paper proposes an AF scheme with multi-device storage based on Reed-Solomon codes by combining data hiding and data destruction. The data is divided into multiple out-of-order data blocks and parity blocks, where these blocks are stored separately in different devices. This method can reduce the storage cost and protect the privacy of data. Even if the data is destroyed, it allows AF investigators to recover the data. Security analysis showed that this AF method can prevent malicious, erroneous or invalid files while acquired and ensure data security in data stolen. Theoretical analysis indicated that this method was difficult for investigators but easy for AFer in files recovery. Experimental results demonstrated that the proposed method is effective and has practical efficiency.

关键词： Anti-forensics Digital forensics Encryption Computer crime distributed storage Erasure coding

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