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检索条件"任意字段=Conference on Optomechatronic Computer-Vision Systems II"

共 985 条记录，以下是41-50 订阅

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vision-Based Corrosion Identification Using Data-Driven Semantic Segmentation Techniques

Vision-Based Corrosion Identification Using Data-Driven Sema...

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2023 IEEE International conference on Imaging systems and Techniques, IST 2023

作者： De Figueiredo, Rui Pimentel Bogh, Simon Aalborg University Department of Materials and Production Aalborg Denmark

ISBN: (纸本)9798350330830

Corrosion is a natural process that degrades metal-made materials. Its detection is of primordial importance for quality control and for ensuring longevity of metal-made objects in various contexts, in particular in industrial environments. Different techniques for corrosion identification including ultrasonic testing, radio-graphic testing, and magnetic flux leakage have been proposed in the past. However, these require the use of costly and heavy equipment onsite for successful data acquisition. An under-explored alternative is to deploy conventional lightweight and inexpensive camera systems and computer vision based methods to tackle the former problem. In this work we present a detailed benchmark of four state-of-the-art supervised semantic segmentation techniques, for vision-based pixel-level corrosion identification. We focus our study on four, recently proposed deep learning architectures which have surpassed human-level accuracy on various visual tasks. The results demonstrate that the former approaches may be used for the problem of segmenting highly irregular patterns in industrial settings, such as corrosion, with high accuracy rates. © 2023 IEEE.

关键词： computer vision

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Towards Automated Visual Inspection of Electrical Grid Assets for the Smart Grid - An Application to HV Insulators

Towards Automated Visual Inspection of Electrical Grid Asset...

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2024 International conference on Communications Control and Computing Technologies for Smart Grids

作者： Lopes, Fernando Rocha, Pedro Coelho, Andre Polytech Univ Coimbra RuaMisericerdiaLagar CorticosS. Martinho Bispo P-3045093 Coimbra Portugal Inst TelecomunicaOes Pinhal Marrocos Polo II DEEC P-3030290 Coimbra Portugal EDP Lab Inspecdo Ativos P-2685038 Sacavem Portugal

ISBN: (纸本)9798350318562;9798350318555

The globally growing demand for electric power, projected to double by 2050, requires extensive upgrades in Transport and Distribution (T&D) systems. Despite the need for new infrastructures to address this demand, the reliability of ageing electrical T&D systems remains a more critical concern, where power line insulators account for over 50% of T&D maintenance costs. To boost inspection efficiency, electric utilities are integrating remote inspection technologies into their operations. However, the processing of the growing volume of collected data is currently strongly limited by human interpretation tasks. This research evaluates the performance of computer vision based on deep learning models for automated visual inspection of electrical grid assets, validated with real-world data. The developed work presents an example application of automated visual inspection of HV insulators, detecting defects in disc insulators in visible light images, using two state-of-the-art deep learning models. YOLOv8s achieved a mAP@50 of 87.9%, while Faster R-CNN X101-FPN achieved 87.2% for the same metric. The findings highlight the advantages and limitations of automated visual inspection, enabling utility companies to benefit from higher efficiency inspection processes, reducing the costs and improving the reliability of electrical grid maintenance. Assessing the performance and complexity of data-driven automated visual inspection techniques is crucial for developing streamlined models that effectively handle high data volumes, and that can evolve to real-time operation and integration in the monitoring and control functions of the smart grid or as a dynamic component of a digital twin of the grid.

关键词： Asset Inspection HV Insulator computer vision Deep Learning Smart Grid

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A Novel ConvNet Architecture for Recognizing Offline Handwritten Gujarati Conjuncts 4th

A Novel ConvNet Architecture for Recognizing Offline Handwri...

引用

4th International conference on Innovations in Computational Intelligence and computer vision, ICICV 2024

作者： Parikh, Megha N. Desai, Apurva A. Department of Computer Science Veer Narmad South Gujarat University Surat India

ISBN: (纸本)9789819769940

Convolutional neural networks have emerged as a crucial tool in computer vision applications. In the present study, we have proposed a novel convolutional neural network model for the recognition of handwritten conjuncts of the Gujarati script. The model is developed from scratch and trained using the participating preceding and succeeding components of the conjuncts. Prior to training, meticulous segmentation of conjuncts is performed to retrieve these components. The scope of this study includes 767 distinct conjuncts, providing a robust foundation for evaluating the model's performance across a varied set of linguistic contexts. The recognition accuracy attained for the preceding components of the conjuncts is 95.66%, while the succeeding components exhibit a recognition accuracy of 95.54%. The results of this study are significant as they demonstrate the effectiveness of a convolutional neural network model in recognizing complex handwritten conjuncts in an under-resourced language like Gujarati. It is an attempt to set a standard for recognizing offline handwritten Gujarati conjuncts by employing cutting-edge deep learning techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Pioneering Pain Relief: Exploring Neuromodulation with Electrical Impulses and Mechanical Techniques for Effective Pain Management 6

Pioneering Pain Relief: Exploring Neuromodulation with Elect...

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6th International conference on Intelligent systems and computer vision, ISCV 2024

作者： Radouane, Asmaa Touil, Mohamed Kadil, Youness Rahmoune, Imane Filali, Houda Hassan II University Laboratory of Pharmacology and Toxicology Faculty of Medicine and Pharmacy of Casablanca Casablanca Morocco Hassan II University EEIS Laboratory Enset Mohammadia Casablanca Morocco

ISBN: (纸本)9798350350180

According to a World Health Organization report, chronic pain is becoming a widespread issue in the larger context of global health, impacting about 20% of the world's population. This continuous problem includes several types of chronic pain that significantly affect people's everyday lives and overall health. Neuromodulation - the use of neuromodulators, electrical and mechanical stimulation - is at the forefront of innovative pain management strategies. Here we examine recent advances in the effects of neuromodulation on three different types of pain, such as dental neuralgia and orofacial pain, osteoarthritis, which is considered to be one of the most painful sensations, and the pain associated with Parkinson's disease. To carry out this study, we undertook an in-depth exploration of the relevant scientific literature. Our approach consisted of carefully reading and understanding numerous articles from major databases. Each article was carefully read through to ensure that the methodology, findings, and consequences were understood. The amalgamation of this data has facilitated the cultivation of a refined comprehension of the present status of knowledge in this particular field, establishing the fundamental groundwork for subsequent investigations in this domain. Our in-depth analysis of the effect of electrical stimulation through various neuromodulation methods reveals compelling insights into advances in the field of pain management. Through this review, we present the results of different methods, highlighting their efficacy and contributions to chronic pain relief, showing their ability to modulate in a complex way the neural circuits associated with pain processing. This review highlights the paradigm shifts in pain treatment facilitated by neuromodulation. The controlled application of electrical impulses offers a promising route to personalized and effective relief for people suffering from chronic pain. © 2024 IEEE.

关键词： Disease control

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computer vision-based Applications in Modern Cars for safety purposes: A Systematic Literature Review 7

Computer Vision-based Applications in Modern Cars for safety...

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conference on Information-Communications-Technology-and-Society (ICTAS)

作者： Nkuzo, Lwando Sibiya, Malusi Markus, Elisha Cent Univ Technol Dept Elect & Elect Engn Bloemfontein South Africa

ISBN: (纸本)9781665489300

Human error, fatigue, and negligence cause the majority of road accidents. Modern automobiles are outfitted with advanced driver assistance systems (ADASs) to help drivers and other vehicle occupants improve safety, enforce the law, and provide comfort. The purpose of this paper is to identify research gaps by highlighting the challenges of computer vision-based application techniques in modern automobiles for safety purposes. This study will also highlight publicly available datasets that can be used for research purposes. As a guideline, the study uses the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA 2020) protocol. Our study drew on seventy sources of literature, fifty of which focused on modern car applications (Lane, Pedestrian, and Traffici sign detection) and 20 on publicly available datasets. Using search criteria, the literature was mined in Google Scholar and IEEE Explore. The boolean operators and keywords listed below were employed. The inclusion and exclusion criteria used in the study are detailed in Section ii. To understand the research gaps between the presented applications and the availability of public datasets, a comparison analysis was performed. Deep learning techniques are more accurate and robust than traditional computer vision techniques, according to the results. The results also show that there are available public datasets. The study, however, was restricted to English papers, lane, pedestrian, and traffic sign applications. Other languages and applications could be future research topics.

关键词： ADAS computer vision Lane detection Traffic sign detection Pedestrian detection Safety Law enforcement

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V-DETR: Pure Transformer for End-to-End Object Detection 16th

V-DETR: Pure Transformer for End-to-End Object Detection

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16th Asian conference on Intelligent Information and Database systems (ACiiDS)

作者： Dung Nguyen Van-Dung Hoang Van-Tuong-Lan Le Hue Univ Hue Univ Sci Hue City 530000 Vietnam HCMC Univ Technol & Educ Fac Informat Technol Ho Chi Minh City 720000 Vietnam Hue Univ Dept Acad & Students Affairs Hue City 530000 Vietnam

ISBN: (纸本)9789819749843;9789819749850

In the field of computer vision, the task of object detection is one of the important tasks, with many challenges and practical applications. Its task is to classify and determine the location of objects in images or videos. Many machine learning methods, especially deep learning, have been developed to perform this task. This article introduces a model combining DETR (DEtection TRansfomer) and ViT (vision Transformer) as a method to recognize objects in images/videos that only use components of the Transformer model. The DETR model achieves good results in object detection using the Transformer architecture and without the need for complex intermediate steps. The ViT model, a Transformer-based architecture, has brought about a breakthrough in image classification. Combining both architectures opens exciting prospects in computer vision. The input image automatically extracted features using the ViT model previously trained on the ImageNet21K dataset, then the features will be fed into the Transformer model to find the classification and bounding box of the objects. Experimental results on test data sets showthat this combined model has better ability in object recognition than DETR and ViT alone. This brings important prospects for the application of the Transformer model not only in the field of natural language processing but also in the field of image classification and object detection. The results of our proposed model have quite high mAP@0.5= 0.444 accuracy, slightly better than the original DETR model. The code is available at https://***/nguyendung622/vitdetr.

关键词： computer vision Object Detection Classification Convolutional Neural Networks Deep Learning DETR ViT

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Fast and Lightweight UAV-based Road Image Enhancement Under Multiple Low-Visibility Conditions

Fast and Lightweight UAV-based Road Image Enhancement Under ...

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2023 IEEE International conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Kapoor, Chaitanya Warrier, Aadith Singh, Mohit Narang, Pratik Puppala, Harish Rallapalli, Srinivas Singh, Ajit Pratap Bits Pilani Department of Electrical and Electronics Engineering Rajasthan Pilani India Bits Pilani Department of Mechanical Engineering Rajasthan Pilani India Bits Pilani Department of Computer Science and Information Systems Rajasthan Pilani India Srm University Ap Department of Civil Engineering Andhra Pradesh Guntur India Bits Pilani Department of Civil Engineering Rajasthan Pilani India

ISBN: (纸本)9781665453813

The amalgamation of Unmanned Aerial Vehicle (UAV) based systems with models built on Artificial Intelligence (AI) and computer vision approaches have enabled several applications in urban planning and smart cities, such as remote health monitoring of roads and infrastructure. However, most of such existing models are trained and evaluated for clear lighting conditions, and they do not perform well under low visibility. This work proposes a fast and lightweight approach for deployment on UAV-based systems that can (i) detect the low-visibility condition in a road image captured by a UAV, and (ii) alleviate it and enhance the quality of the road image. The proposed approach achieves state-of-the-art results and thus establishes itself as an essential precursor to downstream computer vision tasks related to remote monitoring of roads, such as identification of different distress conditions. © 2023 IEEE.

关键词： Image enhancement

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GRAPH NEURAL NETWORKS FOR 3D OBJECT RECOGNITION AND RECONSTRUCTION: A COMPARATIVE STUDY 9

GRAPH NEURAL NETWORKS FOR 3D OBJECT RECOGNITION AND RECONSTR...

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9th International conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th International conference on Connected Smart Cities, CSC 2024 and the 16th International conference on e-Health, EH 2024, Part of the 18th Multi conference on computer Science and Information systems 2024, MCCSIS 2024

作者： Miftah, Rajaa Hanoune, Mostafa LTIM Laboratory Faculty of Science Ben M’sik Hassan II University of Casablanca Casablanca Morocco

ISBN: (纸本)9789898704597

This paper presents a comprehensive analysis and comparative study of existing approaches utilizing Graph Neural Networks (GNNs) for 3D object recognition and reconstruction, alongside other methods existing in the literature. Various architectures, and training strategies proposed in the literature are systematically analyzed, highlighting their strengths, weaknesses, and performance metrics. Through this comparative study, we aim to provide insights into the current state-of-the-art techniques and identify potential avenues for future research in this rapidly evolving field. By delving into the intricacies of GNN applications for 3D object recognition and reconstruction, we aim to contribute to the advancement of intelligent systems capable of navigating the complexities of 3D data. Our findings not only enrich understanding but also serve as a catalyst for innovation, driving forward the frontiers of GNN technology in computer vision and beyond. © 2024 Big Data Analytics, Data Mining and Computational Intelligence. All Rights Reserved.

关键词： Graph neural networks

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Drone path optimization in complex environment based on Q-learning algorithm 5

Drone path optimization in complex environment based on Q-le...

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5th International conference on Intelligent systems and computer vision, ISCV 2022

作者： Laoula, El Mehdi Ben Elfahim, Omar Youssfi, Mohamed Bouattane, Omar ENSET University Hassan II of Casablanca 2IACS Laboratory Mohammedia Morocco

ISBN: (纸本)9781665495585

Path planning of intelligent agents in an emergency context is one of the most popular issues within nowadays context. This work proposes an environment acquisition and a path optimization solution based on reinforcement learning. The proposed solution implements Q-Learning algorithm and enables the agent to choose the path that maximizes the reward and minimizes the penalty. When tested in an experiment grid and compared to other solutions the proposed solution proved to be more stable and more efficient. © 2022 IEEE.

关键词： Drones

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Using Support Vector Regression to Predict the Overall Equipment Effectiveness Indicator* 5

Using Support Vector Regression to Predict the Overall Equip...

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5th International conference on Intelligent systems and computer vision, ISCV 2022

作者： Imane, Mjimer Aoula, Es-Saadia Achouyab, El Hassan Universite Hassan II Casablanca Ecole Normale Superieure de l'Enseignement Technique Laboratoire Signaux Systèmes Distribués et Intelligence Artificielle Mohammedia Morocco Universite Hassan II Casablanca Faculte des Sciences et Techniques Mohammedia Laboratory of Condensed Matter Physics and Renewable Energies Mohammedia Morocco

ISBN: (纸本)9781665495585

This study aims to predict the performance of a company measured using the overall equipment effectiveness (OEE), considered one of the key performance indicators used to measure the performance of a manufacturing system. The prediction of the OEE indicator will be done using a supervised learning technique named: support vector regression (SVR), known for its high prediction accuracy and rapid training speed, SVR is an efficient tool in real-value function estimation. A case study is conducted on this work and the model accuracy is 87%. © 2022 IEEE.

关键词： Forecasting

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