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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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Metal surface Feature Fusion for Defect Recognition 3

Metal surface Feature Fusion for Defect Recognition

引用

3rd international conference on Robotics, Automation and Intelligent control, ICRAIC 2023

作者： Lyu, Wenkai Yang, Chengzhuan Zhejiang Normal University School of Computer Science and Technology Zhejiang China Zhejiang Normal University Key Laboratory of Intelligent Education Technology and Application Zhejiang Province Zhejiang China

ISBN: (纸本)9798350360813

Metal sheets have been widely used in various industrial production and production life aspects. However, the surface of metal plates is prone to multiple defects, which affect the electromagnetic characteristics and coating effect of metal plates. Currently, mainstream inspection uses YOLO, PaddleClas, and other methods to detect flat metal surfaces. These algorithms are all very potent in their classification capabilities, but as generalized defect handling algorithms, they cannot be specific to each type of defect in a real problem. In this paper, we propose a multi-information fusion method for defect recognition. We consider both low-level texture features and high-level convolutional features of metal surfaces for defect recognition. We use a convolutional neural network as the main body and add extracted and processed prompts during training. Finally, we test the proposed method extensively on Camshaft and Kaggle metal surface defects datasets, and the experimental results demonstrate that our method achieves a state-of-the-art recognition performance that surpasses previous defect recognition methods. © 2023 IEEE.

关键词： Convolution

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Research on Multi-scale Network computer Modeling based on Mobile Vendor Management 3

Research on Multi-scale Network Computer Modeling based on M...

引用

3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Yang, Yufan Dong, Xingchen Li, Yingjun Zhang, Huaiyang Shanghai University Sydney Institute of Language and Commerce Shanghai20091 China Northeast Agricultural University College of Economics and Management Harbin150036 China School of Finance and Trade Zhuhai College of Science and Technology Zhuhai519000 China North Minzu University College of Foreign Languages Yinchuan750030 China

ISBN: (纸本)9781665459112

Harsh working environment and living conditions lead workers to become hawkers, and to manage the vendor economy, a significant number of Street Vendor Economic Governance Models have appeared. These models have a particular influence on the operational benefit of the vendor economy. This paper studies the influence of multi-scale network technology regulation mechanisms and incentive mechanisms on the stallholder economy. A model based on the OFRTB network is proposed. The model accurately simulates the transaction process in a multi-scale network flow by adding a new state of supervision node. Finally, we discuss and analyze each state's transition process and probability in the OFRTB model through experimental simulation. This study is helpful to improve the management of mobile vendors further and improve the efficiency of government services. © 2022 IEEE.

关键词： computer networks

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Research and Application of computer vision and Predictive Maintenance in Health Management of Conveying Equipment 3rd

Research and Application of Computer Vision and Predictive M...

引用

3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Long, Zhongyi Gao, Fan Tang, Li Hunan Valin Xiangtan Iron and Steel Co. Ltd. Xiangtan China ChongQing ChuanYi Automation Co. Ltd. Chongqing China

ISBN: (纸本)9789819616930

As an indispensable part of realizing mechanization and automation of handling system in ports, metallurgy, chemical, thermal power, building materials and other fields, conveying equipment faults account for 50% of delivery system faults. In order to improve the maintenance mode of conveying equipment based on manual inspection and record, this paper deals with the research and application of computer vision and predictive maintenance in health management of conveying equipment. Firstly, based on the research of mechanical structure of conveying equipment drive unit, failure cause analysis of conveying equipment and overview of common detection techniques were introduced. Secondly, an health management system based on computer vision and predictive maintenance was designed, the computer vision model was built based on Pangu large-sized model and trained by artificial intelligence training platform, and StressWave analysis method was applied for predictive maintenance of conveying equipment driving unit. In the application case of main belt of blast furnace and the key belt of sintering in the iron making process, this health management system issue alarms accurately and timely, minimize the impact of unplanned downtime on the production process, resulting in significant economic and social benefits. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive maintenance

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MobileHCI 2024 Adjunct proceedings - Publication of the 26th international conference on Mobile Human-computer Interaction

MobileHCI 2024 Adjunct Proceedings - Publication of the 26th...

引用

26th international conference on Mobile Human-computer Interaction, MobileHCI 2024

ISBN: (纸本)9798400705069

The proceedings contain 42 papers. The topics discussed include: a touch of gold – spraying and electroplating 3D prints to create biocompatible on-skin wearables;understanding technological needs of Nigerians towards community policing engagement: an interview-based study;DriveStats: a mobile platform to frame effective sustainable driving displays;exploring trigger-action programs for designing self-control tools in mobile devices;train me: exploring mobile sports capture and replay for immersive sports coaching;eyes-free circular gestures on smartphones;vision beyond boundaries: an initial design space of domain-specific large vision models in human-robot interaction;towards detecting and mitigating cognitive bias in spoken conversational search;and exploiting air quality monitors to perform indoor surveillance: academic setting.

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Real-Time control Applications Using Rapid Modeling Based on Surface Electromyography 3

Real-Time Control Applications Using Rapid Modeling Based on...

引用

3rd international conference on Mechatronics and Automation Technology, ICMAT 2024

作者： Gong, Fenghao Bai, Dianchun Liu, Zhuo Li, Ruiji Lin, Zhongpeng Shenyang University of Technology China University of Electro-Communications Japan

ISBN: (纸本)9781643685649

Surface electromyography (sEMG) directly reflects muscle activity and has been widely applied in areas such as prosthetics, rehabilitation, exoskeletons, and human-computer interaction. However, traditional sEMG modeling methods are often constrained by the complexity of data and computation time, making it difficult to meet real-time control requirements. This paper proposes a rapid modeling method based on sEMG, exploring its application in hand assistive devices and medical rehabilitation. By designing sEMG datasets, extracting transient features, and using deep learning algorithms (vision Transformer and ConvNext), the method effectively reduces modeling time and improves classification accuracy. Experimental results demonstrate that this method is both efficient and accurate in real-time control applications, making it suitable for various real-time interactive systems. © 2024 The Authors.

关键词： Deep learning

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Improved Performance of Canny Edge Detection in Low-Light Conditions 3

Improved Performance of Canny Edge Detection in Low-Light Co...

引用

3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Dadi, Pradeep Surya Saranya, G. Tamilvizhi, T. Nelson, Leema Surendran, R. Chennai Institute of Technology Department of Mechatronics Engineering Chennai India Amrita Vishwa Vidyaapeetham Chennai Campus Amrita School of Computing Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462167

Visual simultaneous localization and mapping (SLAM), which has numerous potential uses, is swiftly emerging as a significant development in embedded vision. In our analysis, there remain several unsolved dilemmas about SLAM that are visual, making it less favored over Lidar SLAM. Because it offers an affordable solution for mapping and navigation in mobile robots. These mobile robots can be defined as robots whose unique base is not fixed, so as technology establishes and advances, we. In this report, a real-time light, that is, dim or image or video in low light problem enhancement algorithm is proposed that significantly improves the overall performance of the Canny Edge part detection algorithm, which usually may be useful for benefit recognition in low light issues in cellular robots for several mapping and navigation tasks. © 2023 IEEE.

关键词： Mobile robots

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A Edge-Guided Satellite Image Semantic Segmentation Method for Real Estate Appraisal 3

A Edge-Guided Satellite Image Semantic Segmentation Method f...

引用

3rd international conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2024

作者： Cui, Yinuo He, Yilin Zhu, Fangyuan School of Economics and Management Nanchang Hangkong University Nanchang China School of International Education Nanchang Hangkong University Nanchang China

ISBN: (纸本)9798331530280

This paper proposes an edge-guided satellite image semantic segmentation method for real estate appraisal. The digitization of real estate appraisal has been significantly enhanced by advancements in computer vision technologies, enabling automated recognition and analysis of property features from satellite images, aerial photographs, and street view images. However, current methods face limitations due to the resolution and quality of satellite imagery, which affect the precision of evaluations. To address these challenges, this study integrates semantic segmentation with a mathematical house pricing valuation model. The Edge Graph Attention U-Net is introduced, focusing on the edges and details of property features to enhance seg- mentation precision. The proposed semantic valuation algorithm integrates segmentation results with traditional market data and Geographic Information System data, employing mathematical model for property value prediction. Experimental results demon- strate the efficacy and accuracy of the proposed model across multiple cities and property types, providing a robust solution for modern real estate appraisal. The primary contributions include improved edge detection for segmentation, integration of detailed segmentation with valuation algorithms, and validation of the model's effectiveness in realworld applications. © 2024 IEEE.

关键词： Satellite imagery

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Sports Game Classification and Detection Using ResNet50 Model Through Machine Learning Techniques Using Artificial Intelligence 3

Sports Game Classification and Detection Using ResNet50 Mode...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Gill, Kanwarpartap Singh Anand, Vatsala Malhotra, Sonal Devliyal, Swati Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350381931

The intention of artificial intelligence (AI) sports analysis is to use AI and machine learning to get useful insights and enhance sports performance. Data from sporting events, players, and welfare equipment are processed and analysed using AI methods. Machine learning, computer vision, natural language processing, and other forms of artificial intelligence analysis are used to improve every facet of sports, from player efficiency to audience participation. It's a fast-paced, ever-changing industry that's revolutionising the way athletes, coaches, and fans interact with the sporting world. The ResNet-50 model and other machine learning techniques are at the bleeding edge of research into sports analysis and performance enhancement. The deep learning model ResNet-50 is used to analyse video and stills from sporting events. Application areas include player monitoring, injury prediction, and game result predicting, all of which are made possible by the use of transfer learning to collect subtle details in sports data. This approach ushers in a new age of data-driven decision making in the sports industry while also revolutionising sports analytics, providing access to invaluable information for coaches, players, and spectators. The primary objective of this research was to categorise nine distinct sports-related picture classifications and predict their expected accuracy, which came in at 93 percent. Future researchers in this field of study will benefit from the high level of accuracy shown in this work. © 2024 IEEE.

关键词： Sports

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Exploring Deep Learning-Based Visual Localization Techniques 3

Exploring Deep Learning-Based Visual Localization Techniques

引用

2023 IEEE 3rd international conference on Data Science and computer Application, ICDSCA 2023

作者： Yu, Zhuoyi Maynooth University International Engineering College Fuzhou University Fuzhou China

ISBN: (纸本)9798350341546

This comprehensive review paper explores the state of the art in visual localization and navigation, drawing on the principles and methodologies of three significant deep learning networks: Visual Localization Network (VLocNet), Deep Fusion Network (DFNet), and Hybrid Frontend Network (HFNet). Each of these networks demonstrates the application of deep learning to spatial awareness and navigation tasks in unique and significant ways. Rather than an exhaustive dissection of these networks, the paper provides an encompassing overview, illuminating their underlying principles, architectural design, and their relative performance within the field. Additionally, the paper delves into the practical implications of these networks, examining their applications in diverse real-world scenarios. It underlines this examination with a comprehensive analysis of existing literature and experimental results, intended to impart a profound understanding of these networks' strengths, limitations, and potential application areas. Ultimately, this review aims to present a valuable compass to researchers navigating the evolving landscape of advancements in visual localization and navigation, thereby fostering enriched understanding and facilitating future exploration and development in this compelling field. © 2023 IEEE.

关键词： computer vision Deep learning Object recognition Robotics Visual localization Convolutional neural networks

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Fabric Defect Detection using Deep Learning 3

Fabric Defect Detection using Deep Learning

引用

3rd international conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Kailasam, Khushi Singh, Jaswant Gopika, G.S. Sreekrishna, M. Supriya, S. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Fabric defect detection plays a crucial role in the textile manufacturing industry's quality control process since it guarantees that customers will receive perfect products. Deep Learning (DL) models have demonstrated a great deal of promise in recent years for accurately identifying fabric defects. The proposed fabric defect identification method combines the best features of two architectures: You Only Look Once (YOLOv4) and Region-based Convolutional Neural Network (R-CNN). While YOLOv4 offers quicker and more effective detection, R-CNN generates region recommendations that allow accurate localization of fabric defects. There are two stages in the proposed system. Initially, region proposals are detected using the R-CNN feature extractor and then a convolutional neural network is used for defect classification. YOLOv4 is used for performing object detection in the second step, where the flaws are concurrently classified and localized. The tests carried out on a fabric defect dataset show that the proposed method outperforms the R-CNN and YOLOv4 models separately. The proposed system is appropriate for real-Time fabric flaw detection applications in a variety of sectors due to its high detection accuracy and quick processing time. This study demonstrates how well R-CNN and YOLOv4 together work for fabric flaw detection, providing hope for future developments in the area. © 2024 IEEE.

关键词： Object detection

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