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检索条件"任意字段=Proceedings of the 3rd International Conference on Control and Computer Vision"

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Advanced Object Detection Algorithms and Its Application Based on Deep Learning 3

Advanced Object Detection Algorithms and Its Application Bas...

引用

3rd international conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Ma, Bohan Zheng, Chengyu Zheng, Zhuowen Henan Normal Univ Xinxiang Henan Peoples R China Ohio State Univ Columbus OH 43210 USA Harbin Inst Technol Shenzhen Shenzhen Peoples R China

ISBN: (纸本)9781665417907

Object detection is one of the most basic and challenging problems in such a field, which has become a paramount topic for scholars in recent years. In the past two decades, object detection has developed rapidly from the beginning to the application in all aspects of life, with the improvement both in detection accuracy and detection speed. In this paper, firstly, we focus on the research progress of object detection algorithm based on deep learning in the light of its technical evolution and application. Secondly, we compare and analyze the two-stage and single-stage detection framework from series algorithms based on R-CNN to series algorithms based on Yolo, and introduce the common data sets and index evaluation as well as the application process of the algorithm in the detection fields of pedestrian, face, text, medical image, sign language, etc. Finally, we predict the prospects of deep learning-based object detection algorithms according to the existing challenges.

关键词： deep learning object detection computer vision algorithm

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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

引用

Electronic Engineering (ICEEM), international conference on

作者： Mahmoud Elshabrawy Marwa M. Eid Abdelaziz A. Abdelhamid El-Sayed M. El-Kenawy Abdelhameed Ibrahim Dept. of Communications & Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Dep. of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Engineering and Control Systems Dept. Faculty of Engineering University Mansoura Egypt

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models.

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Cable Fault on-Line Monitoring Based on Transient Traveling Wave Signal Analysis Technology

Cable Fault on-Line Monitoring Based on Transient Traveling ...

引用

Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Wang Li Weiqiang Zhou Lin Fu Tengfei Yuan National Energy Group Hunan Electric Power Co. Ltd Changsha City China National Energy Group Longyuan Jiangyong Wind Power Co. Ltd Shaoyang City China National Energy Group Hunan Electric Power New Energy Co. Ltd Changsha City China

This paper mainly studies a fault identification and location technology for optical cable lines using transient traveling wave mode maxima method, which mainly includes the following steps: real-time monitoring and synchronous upload of traffic information; Information collection and preservation; Cable fault identification, including: current oscillogram drawing, fractal box dimension calculation, current information space transformation, discrete wavelet transform and wavelet coefficient calculation for flow information in mode space, inspection of maximum point of initial traveling wave mode of flow information, and cable fault identification; Cable fault location, including: drawing of current oscillogram, spatial transformation of current information β The module voltage component and the module voltage component of the flow information on the load side are calculated using discrete wavelet transform and wavelet coefficients. The maximum point of the initial traveling wave mode of the flow information is checked, and the cable fault is located. The technical process of this paper is simple, the calculation method is simple, the accident identification and location efficiency is high, the accuracy is good, the technology is comprehensive, and the application is strong.

关键词： Image motion analysis computer vision Fault location Fractals Space exploration Discrete wavelet transforms Communication cables

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Soil Image Segmentation Based on Mask R-CNN

Soil Image Segmentation Based on Mask R-CNN

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Yida Chen Kang Liu Yi Xin Xinru Zhao College of Computer and Information Science Chongqing Normal University Chongqing China Chongqing Center of Engineering Technology Research on Digital Agricultural Service Chongqing China Agriculture and Rural Committee of Nanan District Chongqing China Agriculture and Rural Committee of Banan District Chongqing China

The complex background in the soil image collected in the field natural environment will affect the subsequent soil image recognition based on machine vision. Segmenting the soil center area from the soil image can eliminate the influence of the complex background, which is an important preprocessing work for subsequent soil image recognition. For the first time, the deep learning method was applied to soil image segmentation, and the Mask R-CNN model was selected to complete the positioning and segmentation of soil images. Construct a soil image dataset based on the collected soil images, use the EISeg annotation tool to mark the soil area as soil, and save the annotation information; train the Mask R-CNN soil image instance segmentation model. The trained model can obtain accurate segmentation results for soil images, and can show good performance on soil images collected in different environments; the trained instance segmentation model has a loss value of 0.1999 in the training set, and the mAP of the validation set segmentation (IoU=0.5) is 0.8804, and it takes only 0.06s to complete image segmentation based on GPU acceleration, which can meet the real-time segmentation and detection of soil images in the field under natural conditions. You can get our code in the Conclusions.

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Audio-Visual Evaluation of Oratory Skills 3

Audio-Visual Evaluation of Oratory Skills

引用

3rd IEEE international conference on Transdisciplinary AI, TransAI 2021

作者： Michelson, Tzvi Peleg, Shmuel School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel

ISBN: (纸本)9781665434126

What makes a talk successful? Is it the content or the presentation? We try to estimate the contribution of the speaker's oratory skills to the talk's success, while ignoring the content of the talk. By oratory skills we refer to facial expressions, motions and gestures, as well as the vocal features. We use TED Talks as our dataset, and measure the success of each talk by its view count. Using this dataset we train a neural network to assess the oratory skills in a talk through three factors: body pose, facial expressions, and acoustic features. Most previous work on automatic evaluation of oratory skills uses hand-crafted expert annotations for both the quality of the talk and for the identification of predefined actions. Unlike prior art, we measure the quality to be equivalent to the view count of the talk as counted by TED, and allow the network to automatically learn the actions, expressions, and sounds that are relevant to the success of a talk. We find that oratory skills alone contribute substantially to the chances of a talk being successful. © 2021 IEEE.

关键词： Quality control

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Secrecy Rate Threshold VS. An Optimum Power Splitting Factor Using Beamforming Technique in PHY Layer Security for MU-MIMO Systems

Secrecy Rate Threshold VS. An Optimum Power Splitting Factor...

引用

Sciences and Techniques of Automatic control and computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Abeer A S Elhoula Marai M. Abousetta Tarek Saleh M. Ghmati Department of Electrical and Computer Engineering School of Applied Science and Engineering Libyan Academy Department of Electrical and Computer Engineering Al-Graboulli Faculty of Engineering Elemrgib University

This paper describes a new approach to the problem of interception of wireless communication channels between the legitimate users. Physical PHY Layer Security (PLS) is new topic enhancing the secrecy performance of a Multi-User Multiple-Input-Multiple-Output (MU-MIMO) system for wireless communication from a single base-station to many users. Beamforming techniques such as "Pre-Maximum Ratio Combining (P-MRC) and Pre-Equal Gain Combining (P-EGC)" with assumption of perfect Channel State Information CSI is considered in order to achieve the perfect secure transmission between legitimate users. Along with beamforming, Artificial Noise (AN) is a technology that is being studied. The plotted achievable worst-case of secrecy rate down to the best secrecy rate value (threshold of secrecy rate) versus the power allocation fractions (The power splitting factor). The achieved threshold of secrecy rates for the implemented MU-MIMO were used by Matched Filter (MF) beamforming schemes (P-MRC) and (P-EGC) and suggested that the (P-MRC) is considered as an optimal beamforming technique in order to achieve a best secrecy rate or best threshold of secrecy rate for a nearest Information User (IU) to the Eavesdropper Eve.

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Real-Time Monitoring and control System of Water Parameters for Paiche Cultivation

Real-Time Monitoring and Control System of Water Parameters ...

引用

Electrical, computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Manuel Michael Beraun-Espiritu Alexander Yordy Vidal-Barbaron Crhistian Edgar Huamán-Laura Alem Huayta-Uribe Lizbett Nattaly Mantari-Ramos Karolina Solorzano-Pomachagua Carlos Alberto Coaquira-Rojo School of Mechatronic Engineering Universidad Continental Huancayo Perú

The arapaima, also known as paiche or pirarucu is one of the largest freshwater fish in the Amazon, in which its cultivation has been growing progressively due to consumer demand. This research presents the design of a real-time monitoring and control system of water parameters for paiche farming, which employs mechatronics concepts using the VdI-2206 methodology. The study controls and monitors the water's temperature, dissolved oxygen, and pH. Sensors obtain the values for each parameter, process them by a PLC, and project them on an HMI interface. The environment is programmed based on the information provided by FONDEPES, which mentions that the water temperature for paiche cultivation must be between 26°C to 31°C, the pH level between 6 and 8 UI, and dissolved oxygen with a value greater than or equal to 4 mg/L. In addition, the proposed mechatronic system aims to be a user-friendly system, where the primary function is the monitoring and controlling water parameters for paiche cultivation, resulting in the correct functioning of the system, thus concluding the feasibility of implementation and the contribution to the development of new research.

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A Human Ear Reconstruction Autoencoder 16

A Human Ear Reconstruction Autoencoder

引用

16th international Joint conference on computer vision, Imaging and computer Graphics Theory and Applications (VISIGRAPP) / 16th international conference on computer vision Theory and Applications (VISAPP)

作者： Sun, Hao Pears, Nick Dai, Hang Univ York Dept Comp Sci York N Yorkshire England Mohamed bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9789897584886

The ear, as an important part of the human head, has received much less attention compared to the human face in the area of computer vision. Inspired by previous work on monocular 3D face reconstruction using an autoencoder structure to achieve self-supervised learning, we aim to utilise such a framework to tackle the 3D ear reconstruction task, where more subtle and difficult curves and features are present on the 2D ear input images. Our Human Ear Reconstruction Autoencoder (HERA) system predicts 3D ear poses and shape parameters for 3D ear meshes, without any supervision to these parameters. To make our approach cover the variance for in-the-wild images, even grayscale images, we propose an in-the-wild ear colour model. The constructed end-to-end self-supervised model is then evaluated both with 2D landmark localisation performance and the appearance of the reconstructed 3D ears.

关键词： Ear 3D Ear Model 3D Morphable Model 3D Reconstruction Self-supervised Learning Autoencoder

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Research on Building Crack Width Monitoring Method Based on computer vision

Research on Building Crack Width Monitoring Method Based on ...

引用

Signal Processing, Communications and Engineering Management conference (ISPCEM), international

作者： Houyan Wang Shuoting Xiao Ningbo Youzuo Construction Technology Company Ningbo Zhejiang China Institute of Civil Engineering and Architecture Ural Federal University Ekaterinburg Russia

As construction projects enter a new era, building crack width monitoring has entered a stage of high-quality development. It is necessary to propose image processing propositions that better meet the needs of crack width monitoring for building safety by focusing on the deep learning idea with image segmentation as the core. Based on the dynamic evolution of image processing development, according to the internal logic of the convolutional neural network, a theoretical analysis framework for the development of image segmentation is constructed. It can explain the image segmentation development mechanism jointly generated by the image processing mechanism and the optimization loop mechanism involved in image sampling and segmentation. From the perspective of quality change and practical deduction of image segmentation development, the possibility of moving towards the high-quality development goal of building crack width monitoring is further explored. The purpose of developing building crack width monitoring is to provide crack width estimates that meet expected standards for construction projects, which is committed to continuously improving the quality of crack width and improving the satisfaction of crack width monitoring. Therefore, it is necessary to strengthen the image segmentation control based on the quality of the inner loop of the convolutional neural network. Establishing an interaction and feedback mechanism between image segmentation and perception of the quality of a construction project, as well as establishing an evaluation system for image segmentation and monitoring of the crack width of a building, which will achieve high-quality development of building crack width monitoring, promote construction project safety, and truly meet the needs of construction projects.

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A new appearance-based gaze estimation via multi-modal fusion

A new appearance-based gaze estimation via multi-modal fusio...

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Neural Networks, Information and Communication Engineering (NNICE), international conference on

作者： Huijie Yang Zuoyun Yang Jiahui Liu Jiannan Chi School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing China

Appearance-based gaze estimation has gained more and more attention because of its generality, robustness, and subject independence. Deep learning, which has made a great deal of success in computer vision, has also greatly improved the accuracy of appearance-based gaze estimation. To further reduce the error in gaze estimation, we focus on extracting better feature information from eye and face images. In this paper, we propose a novel multimodal fusion gaze estimation model based on ConvNext and dilated convolution. In this model, the eye image and face image are used as input, and the ConvNext network is used to extract the features of the face image and the eye features are extracted by a dilated convolution-based network, and the feature map of the two images are fused using the fully connected layer to perform gaze estimation. In the experimental part, the designed model is verified on the public dataset MPIIGaze, and compared the proposed model with other gaze estimation models. The experimental results show that our proposed method has greatly improved the accuracy of gaze estimation on the MPIIGaze dataset compared to other related works. Our proposed multimodal fusion gaze estimation model achieves state-of-the-art result on the MPIIGaze dataset.

关键词： Deep learning Estimation error Convolution Fuses Estimation Feature extraction Robustness

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