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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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Control of Technical Parameters of Industrial Products Through machine vision 43rd

Control of Technical Parameters of Industrial Products Throu...

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43rd IBIMA conference on Artificial intelligence and machine Learning, IBIMA-AI 2024

作者： Gheorghita, Vlad National University of Science and Technology POLITEHNICA Bucharest Romania

ISBN: (纸本)9783031774928

During the production process, a lot of undesirable or faulty products are also created. Since these products cannot be used or distributed on the market, they must be discarded. Product integrity and quality inspection, which is also a result of production process efficiency and optimization, is one of the most important procedures in modern industrial applications. The quickest and most efficient way to make sure products meet the quality requirements is through an automated verification process. These systems are considered as essential by some applications because replacing them with human employees would necessitate a substantial workforce and related costs. This research provides a mechanical experimental system that will be able to verify fast and control the most important parameters of various products using the artificial intelligence approach and machine vision techniques. The cost of the designed system is low and the structure used is clear. The demonstration of the utility of the created product was carried out on a toothed wheel with many essential technical characteristics, a widely used landmark in the field of manufacturing engineering. image processing techniques are used such as threshold function, calibration, pattern matching, measure. The experimental model designed is easy to implement and can represent a transition towards the application of modern industry 5.0 principles in different processing companies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： machine vision

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Real-time detection of road hazards for autonomous vehicle systems

Real-time detection of road hazards for autonomous vehicle s...

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International image processing, applications and Systems conference (IPAS)

作者： Máté András Szabó Anita Keszler László Tizedes Machine Perception Research Lab. HUN-REN SZTAKI Budapest Hungary

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Automated detection of road hazards such as speed bumps, has become an important area of research due to its potential to improve road safety in autonomous driving. Various techniques have been introduced to detect these hazards using camera vision and artificial intelligence-based image processing methods. However, estimating their distance is still challenging. To address this problem and to satisfy the requirement for real-time on-board data processing, the proposed system has the following properties: (1) high-accuracy road hazard detection by analyzing mono-images and videos with a re-trained YOLO neural network; (2) precise distance measurement utilizing a LiDAR; and (3) efficient local data processing using ROS, implemented on an NviDIA Jetson AGX Xavier. An important contribution of this paper is introducing multiple classes of road hazards when training the network, instead of only focusing on speed bumps and potholes. Furthermore we have analyzed different LiDAR technologies (standard rotating and non-repetitive circular scanning) to evaluate and compare their precision and to demonstrate that our method can be successfully applied regardless of the scanning pattern of the LiDAR.

关键词： Laser radar Roads image processing Cameras Data processing Hazards Real-time systems Distance measurement Sensors Autonomous vehicles

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Robust Optimization-based Neural Architectures Against Adversarial Attacks

Robust Optimization-based Neural Architectures Against Adver...

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IEEE SSD International Multi-conference on Systems, Signals and Devices

作者： Khaoula Ben Ali Seifeddine Messaoud Mohamed Ali Hajjaji Mohamed Atri Noureddine Liouane Laboratory of Automatic Signal Image Processing National Engineering School Monastir University Monastir Tunisia Faculty of Sciences of Monastir Monastir University Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse Sousse University Sousse Tunisia Computer Engineering Department College of Computer Science King Khalid University Abha Saudi Arabia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Recent years have seen a rapid development in machine Learning, which has profoundly influenced many areas of science and engineering. Among them, computer vision takes the leading place, where important tasks are image classifications powered by CNNs. Despite the great performance of CNNs in complicated scenarios, they remain sensitive to so-called adversarial attacks, and deliberate perturbations leading them to incorrect predictions. Besides more innocuous consequences, this has serious security implications for critical applications, in-cluding medical diagnostics, where misclassifications might result in disastrous outcomes. This research work discusses adversarial attacks on CNNs and other DNNs in computer vision, studying a full range of the generation and detection methods with details while discussing intrinsic vulnerability and robustness. It also proposes a learning framework that will enhance the robustness and security of DNNs and CNNs against such adversarial perils. The ultimate goal is directed to an improvement in the reliability of such models in absolutely critical scenarios for safe deployment into applications where accuracy is crucial.

关键词： Computer vision Accuracy Perturbation methods Computer network reliability machine learning Robustness Security Medical diagnosis Resilience image classification

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CD-SEM Anomaly Detection Through machine Learning and Generative AI

CD-SEM Anomaly Detection Through Machine Learning and Genera...

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IEEE/SEMI conference and Workshop on Advanced Semiconductor Manufacturing

作者： Susan Sun Xiaoyan Zhuo Phong Nguyen Todd Smith Jinhua Ding Troy Stansell Amit Srivastava Yousef Hanif Data Science Micron Technology Inc. Manassas VA USA Lithography Process Eng Micron Technology Inc. Manassas VA USA Data Science Micron Technology Inc. Singapore Product Quality Assurance Micron Technology Inc. Manassas VA USA

ISBN: (数字)9798331531850

ISBN: (纸本)9798331531867

In a complex semiconductor manufacturing environment, critical dimension scanning electron microscope (CD-SEM) images are captured at metrology step to monitor structural measurements and detect anomaly to meet the stringent process control requirements. This paper focuses on advanced CD-SEM image processing and anomaly detection using machine learning and generative AI models, which include computer vision (CV) image processing, Residual Neural Network (ResNet) deep learning and Generative Adversarial Network (GAN) model for various use cases. The applications of these models during in-line monitoring are crucial to identify potential process issues for yield and quality improvement.

关键词： Deep learning Semiconductor device measurement Generative AI image processing Computational modeling Semiconductor device manufacture Generative adversarial networks Servers Anomaly detection Monitoring

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A New video Summarization Approach Using Object Density Based image Similarity for Smart City applications

A New Video Summarization Approach Using Object Density Base...

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International Scientific-Professional conference on Information Technology (IT)

作者： Turan Goktug Altundogan Mehmet Karakose Fatih Mert Computer Engineering Department Manisa Celal Bayar University Manisa Türkiye Computer Engineering Department Firat University Elazig Türkiye Huawei Telecommunication Foreign Trade Co. Ltd. Istanbul Türkiye

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

Summarization approaches are currently proposed solutions that focus on meaningfully reducing different types of data such as text, audio, and video. Many techniques such as machine learning, signal processing, image processing, computer vision, and deep learning can be used to develop summarization approaches. In this study, we performed object detection on videos that can be used in smart city applications using a pretrained YOLOv8 model. As a result of the object detection, we created a feature vector for each image frame by using the location information covered by the classes used in the object detection process. Then, we used several different approaches to determine the reference feature vector for the video. Finally, we calculated the cosine similarities of the feature vector for each frame to this reference feature vector using different methods. With the method we developed, we presented a similarity-focused summary created by selecting the video frames expressed with maximum similarity. We also developed an evaluation approach to evaluate the summaries we presented, comparing the overall heat maps of the video with the heat maps of the summary videos. Experimental results demonstrate the efficiency of our summarization approaches.

关键词： Deep learning Computer vision Smart cities Computational modeling Object detection Signal processing Feature extraction Vectors Heat maps Information technology

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1st International conference on Artificial Intelligence and its applications, ICAIA 2023

1st International Conference on Artificial Intelligence and ...

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1st International conference on Artificial Intelligence and its applications, ICAIA 2023

ISBN: (纸本)9783031843938

The proceedings contain 37 papers. The special focus in this conference is on Artificial Intelligence and its applications. The topics include: The Hybrid Cardiac Risk Assessment and Prediction Model Using Convolutional Neural Networks;deep Learning applications for Malaria Detection and Diagnosis: A Review;Environmental Considerations in the Ethics of AI Adoption in Healthcare: Striving for Sustainable and Responsible Practices;harnessing the Power of Cognitive Computing: Assessing Point-of-Care Decision Support Tools in Oncology Practice;Critical Analysis in Use of AI in Health Care Management;classification and Prediction of Spinal Tuberculosis Disease Using Optimization of Convolution Neural Network Using Spatial and Temporal Constraints;artificial Intelligence for Remote Healthcare in Underserved Areas: Enhancing Access and Quality of Healthcare Delivery;machine Learning Based Skin Cancer Detection and Recognitions Techniques in IoT Environment;validation of a Chronic Kidney Disease Prediction System Using machine Learning Techniques;real-Time Feedback Detection Using Emotion Detection and Facial Recognition;revolutionizing vision Tasks: Unlocking Potential Through Patch-Based Approaches;automated Knee Implant Identification from 2D Templates Using image processing and Artificial Intelligence – An Experimental Approach;crop Analysis and Classification Based on Phenotype Using Ensemble Learning;Classification and Identification with Health Benefit Assessment and Nutrient Profile of Brewed Tea Utilizing Computer vision with ML and DL and Sensory Approaches;enhancing Industrial Automation Flexibility Through Neural Network-Empowered machine vision applications.

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1st International conference on Artificial Intelligence and its applications, ICAIA 2023

1st International Conference on Artificial Intelligence and ...

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1st International conference on Artificial Intelligence and its applications, ICAIA 2023

ISBN: (纸本)9783031843969

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In-domain Self-supervised Learning for Plankton image Classification on a Budget

In-domain Self-supervised Learning for Plankton Image Classi...

引用

IEEE Winter applications and Computer vision Workshops (WACVW)

作者： Massimiliano Ciranni Ani Gjergji Andrea Maracani vittorio Murino vito Paolo Pastore MaLGa DIBRIS University of Genoa Italy Istituto Italiano di Tecnologia Genoa Italy University of Verona Italy

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

In the last few years, the abundance of available plank-ton images has significantly increased due to advancements in acquisition system technology. Consequently, a growing interest in automatic plankton image classification has surged. machine learning algorithms have recently emerged to assist in the analysis of this vast quantity of data, supporting traditional manual processing. However, annotating such data is costly and demands significant time and resources, thus requiring data-efficient machine learning solutions. The typical framework for tackling this issue has been the adoption of supervised imageNet pre-trained models, and fine-tuning them on the plankton classification downstream task. Nonetheless, self-supervised pre-training protocols may provide an effective alternative to the supervised approaches using imageNet, while allowing the exploitation of the increasingly large amount of unanno-tated plankton data. To the best of our knowledge, no work systematically analyzes the impact of self-supervised pre-training protocols for plankton image classification. To fill this gap, in this paper, we present a thorough comparison between in-domain (plankton images) and out-of-domain (imageNet) supervised and self-supervised pre-training, in terms of the quality of the corresponding embeddings for plankton image classification. We believe that this work may pave the way for further research in self-supervised protocols for the plankton domain, providing a valuable alternative to imageNet, and exploiting the vast amount of unannotated available plankton images.

关键词： Training Computer vision Protocols Systematics Plankton Transfer learning Self-supervised learning Feature extraction Transformers image classification

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vision-Based Autonomous Car Brake and Steering Assistance Using Deep Learning

Vision-Based Autonomous Car Brake and Steering Assistance Us...

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AIML-applications for Engineering & Technology (ICAET), International conference on

作者： Anees H Pooja Agrawal School of Robotics Defence institute of advanced technology Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

This paper presents a novel approach for enhancing vehicle safety and navigation through an integrated system for lane detection, vehicle alignment, and automatic braking using visual feedback. Our proposed system employs advanced deep learning and computer vision techniques with real-time processing to detect the exact boundaries of lane and ensures precise vehicle movement within the lane. The system continuously analyses lane markings and modifies the vehicle's position to ensure optimal lane adherence by utilizing a combination of machine learning algorithms and camera-based image processing. Additionally, the system incorporates an adaptive braking mechanism that identifies vehicles ahead using visual inputs. Furthermore, the jerks experienced during steering alignment can be greatly reduced by the suggested steering control system. The system's efficiency in various driving conditions is evidenced by its experimental simulation results, which also show improvements in collision avoidance and lane-keeping accuracy. This approach contributes to improved driving convenience and road safety by marking a substantial advancement in autonomous driving technologies.

关键词： YOLO Instance segmentation Deep learning visualization Accuracy Simulation Real-time systems Road safety Automobiles Autonomous vehicles

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Enhancing Industrial Automation Flexibility Through Neural Network-Empowered machine vision applications 1st

Enhancing Industrial Automation Flexibility Through Neural N...

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1st International conference on Artificial Intelligence and its applications, ICAIA 2023

作者： George, Navin M. Rosero, Evelyn Mayakannan, S. Shameem, A. Divya, S. Arul, N. Electronics and Communication Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore641114 India Riobamba060155 Ecuador Department of Mechanical Engineering Vidya Vikas College of Engineering and Technology Namakkal Tamilnadu Tiruchengode India AMET Business School AMET University Tamilnadu Chennai India Erode Tamil Nadu Perundurai638057 India Department of Mechatronics Engineering Sona College of Technology Tamilnadu Salem India

ISBN: (纸本)9783031843969

vision-based systems provide non-contact sensory input for processing and feedback, expanding the capabilities of industrial automation applications. Prediction using ANNs aids such conformities in navigating nonlinear computational landscapes. They map the plethora of potential outcomes or zones of uncertainty introduced by the system’s constituents onto a set of feasible options for action. A measure of intelligence is transferred to robotic systems through the use of trained networks. Two uses of machine vision are presented here. The 3 DOF robotic assembly enables precision cutting of fragile materials with the help of visual guiding and pixel elimination. The 6-degrees-of-freedom robot uses a combination of visual feedback from an onboard camera and supervision from an external camera to achieve its goals. Pick-and-place operations are executed with a switching control method. Both systems improved in terms of computing time and convergence once ANN was used to make the strategies intelligent. The retrieved scene image features are put to use by the networks in a variety of ways. The simulation and experimental results that back up the suggested approaches demonstrates the value of artificial neural network in machine vision applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robotic assembly

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