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检索条件"任意字段=Conference on Process Imaging for Automatic Control"

共 467 条记录，以下是11-20 订阅

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Development of low-cost camera-based UAV control strategy

Development of low-cost camera-based UAV control strategy

引用

International automatic control conference (CACS)

作者： Jhan, Zih-Syuan Yeh, Nai-Jui Hsieh, Tzu-Ling Chen, Chang-Yu Chuang, Cheng-Ta Natl Taipei Univ Technol Dept Intelligent Automat Engn Taipei 106344 Taiwan

ISBN: (数字)9781665496469

ISBN: (纸本)9781665496469

Nowadays, UAV can fly well with GPS outdoors. However, when there is no GPS, it is hard to control the UAV. Our control method aims at indoor UAV control, where there is no GPS. We use a down-looking camera and imaging processing approach to control the UAV. Through our method, the UAV can depart and land in the specific area as well as following the path set on the ground. We have our method proven by a simulation environment. For obstacle avoidance task, a front-looking camera and Dronet is employed. Dronet can predict the collision probability. Whenever the probability is too high, the UAV would hover till the obstacle, such as a human, disappears from its vision. Then we compress Dronet to make it perform faster without too much loss of accuracy on embedded computers.

关键词： Computers Costs Computational modeling process control Cameras Production facilities Collision avoidance

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YOLOv5 based Intelligent Defect Detection of Dual-color Gradient Object Fabricated by Fused Deposition Modeling Additive Manufacturing System

YOLOv5 based Intelligent Defect Detection of Dual-color Grad...

引用

2023 International automatic control conference, CACS 2023

作者： Yeh, C.R. Yan, S.Q. Tsai, M.J. National Taiwan University of Science and Technology Graduate Institute of Automation and Control Taiwan

ISBN: (纸本)9798350306354

Quality insurance is a critical process in the manufacturing industry. Additive manufacturing (AM) has gained rapid development due to its capability of producing complex parts. Among various additive manufacturing, Fused Deposition Modeling (FDM) is widely used by consumers and businesses. In recent years, deep learning has been applied to enhance quality inspection in the manufacturing sector by combining traditional AOI with AI, particularly in multi-class defect detection. This paper proposes an intelligent defect detection system for FDM dual-color gradient objects based on the YOLOv5. Traditional inspection methods may struggle to detect defects caused by uneven colors from the shape or material incompatibility of dual-color gradient objects. By integrating an intelligent defect detection system into the production process, accuracy checks and classification can be performed to detect and address issues promptly, thus reducing costs. In this study, an image database for FDM dual-color gradient objects with curved surfaces is established by an automated imaging system equipped with two cameras and two controllable lighting sources to enable multi-angle observation and quickly acquire the image data of training and test samples. The primary objective is to detect nine different samples, categorized into six defect classes: good(normal), curling, break, foreign, gap, and uneven defects. A total of 1914 images are obtained. The images are split into training (80%, 1531 images) and validation (20%, 383 images) sets. Testing sets are also used to evaluate the model's performance. The YOLOv5 model is built and the performance among different depth (s, m, l, x) is compared after training for 100 epochs. The built model achieves performance of over 90% (Mean Average Precision mAP@0.5) for the validation set. Furthermore, the model is tested under on-line FDM fabricating process to verify the application in real-time defect detection in the future. © 2023 IEEE.

关键词： Deposition

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27th International conference on Automation, AUTOMATION 2023

27th International Conference on Automation, AUTOMATION 2023

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27th International conference on Automation, AUTOMATION 2023

ISBN: (纸本)9783031258435

The proceedings contain 22 papers. The special focus in this conference is on Automation. The topics include: Localization of Agricultural Robots: Challenges, Solutions, and a New Approach;the Concept of a Gripper with Pose Estimation for Automotive Components;SpacePatrol - Development of Prospecting Technologies for ESA-ESRIC Challenge;scanning Electrochemical Microscope Based on Visual Recognition and Machine Learning;simulation of Ultrasonic Vibration Propagation Through Resonators for Acoustic Coagulation Intensification;mathematical Model of the Approximate Function as the Result of Identification of the Object of automatic control;regression Analysis on the Values of the Specific Activity of 137Cs in Radioactive Soil Contamination;quasi-Digital Measuring System for Mechanical Quantities;hyperspectral imaging System for Food Safety Inspection;numerical Estimation of the Internal Positivity of the Fractional Order Model of a Two-Dimensional Heat Transfer process;principal Components Method in control Charts Analysis;polynomial Maximization Method for Estimation Parameters of Asymmetric Non-Gaussian Moving Average Models;nanocomposites for Improved Non-enzymatic Glucose Biosensing;FOPID and PID - Comparison of control Quality and Execution Time on the Example of Two Rotor Aerodynamical System;analysis of Sounding Rocket Dispersion Using Monte-Carlo Simulation;Proactive-Reactive Approach to Disruption-Driven UAV Routing Problem;Network Aspects of Remote 3D Printing in the Context of Industry as a Service IDaaS;the Concept of Use of process Data and Enterprise Architecture to Optimize the Production process;autonomous Mobile Flock Traffic Simulation in Digital Twin Mode;neural Network Model for Predicting Technological Losses of a Sugar Factory.

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Detection and Classification of Knee Ligament Pathology based on Convolutional Neural Networks 9

Detection and Classification of Knee Ligament Pathology base...

引用

9th International conference on control, Decision and Information Technologies, CoDIT 2023

作者： Voinea, Ştefan-Vlad Gheonea, Ioana Andreea Selişteanu, Dan Vlǎduţ Teica, Rossy Florescu, Lucian Mihai Ciofiac, Cristina Mihaela Nica, Raluca Elena University of Craiova Department of Automatic Control and Electronics Romania University of Medicine and Pharmacy of Craiova Romania

ISBN: (纸本)9798350311402

Nowadays, medical imaging has almost completely replaced traditional techniques for diagnosis and treatment planning due to their non-invasiveness, speed and ease of manipulation. Due to their popularity the medical imaging pushed the bottleneck towards the medical staff that, post-acquisition, have to analyse each case. This process is not only slow but also subjective to a level that it becomes error prone. The anterior cruciate ligament (ACL) is one of the most injured ligaments of the knee. Injuries occur predominantly in a young and sports-active population. Many patients are left with significant disability following injury to the ACL. The injury leads to alteration in the mechanics of the knee. Because some of the cases require surgery it is imperative to be able to classify and detect ACL pathology with high accuracy. Hence this paper studies the usage of pre-trained convolutional neural networks with residual connections (ResNet), in conjunction with image processing techniques to detect ACL pathology and distinguish between multiple tear levels. The ResNet used is not the vanilla ResNet but rather a modified version adapted to work with 3D volumes composed of 2D images (slices). With an obtained accuracy of 87% it is safe to say that the model's output can be successfully used by radiologists to set a 1st baseline diagnostic without any effort almost instantly. © 2023 IEEE.

关键词： Pathology

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Evaluation of ballistic inserts with THz-TDS imaging

Evaluation of ballistic inserts with THz-TDS imaging

引用

International conference on Infrared and Millimeter Waves

作者： Kamil Kamiński Norbert Pałka Marcin Maciejewski Institute of Optoelectronics Military University of Technology Warsaw Poland

ISBN: (数字)9798350370324

ISBN: (纸本)9798350370331

The paper introduces the concept of automating the defect detection process in ballistic inserts through the use of a fast time-domain spectroscopy (TDS) scanner with electronically controlled optical sampling (ECOPS). The proposed algorithm facilitates the automatic detection of defects at different depths, thereby substantially cutting down analysis time without the need for specialized expertise from the system operator.

关键词： Terahertz wave imaging Spectroscopy process control Optical imaging Time-domain analysis Optical control Defect detection

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Analysis of Free Fatty Acids (FFA) in Palm Oils Based on the Raman Spectra

Analysis of Free Fatty Acids (FFA) in Palm Oils Based on the...

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2023 IEEE International conference on automatic control and Intelligent Systems, I2CACIS 2023

作者： Shaharuzzaman, Siti Nurshaheera Hashim, Fazida Hanim Sajab, Mohd Shaiful Huddin, Aqilah Baseri Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Electrical Electronic and Systems Engineering Selangor Bandar Baru Bangi Malaysia Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Malaysia Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Chemical and Process Engineering Malaysia

ISBN: (纸本)9798350321302

Palm oil is a stable vegetable oil with various uses in both the food and non-food industries. The quality of edible palm oil is measured by its content of free fatty acids (FFA), which should be below 5% to be safely consumed and used, according to The Palm Oil Refiners Association of Malaysia (PORAM). Current titration methods require that the oil sample be dissolved in neutralized ethanol/diethyl, which is a long process that can damage the sample and cause delays and losses in the process. The focus of this study is to analyze the FFA in palm oils based on the Raman spectra of palm oil samples and oil palm fruitlets. Four samples of commercially available palm oils of different qualities, as well as 47 samples of oil palm fruitlets, were used in this study. The Raman spectra of all samples were collected via point scanning using a confocal microRaman imaging spectrometer with a 532 nm laser source. Manual spectrum pre-processing was performed with baseline correction and data filtering using Savitzky-Golay filters. Possible FFA peaks characteristics on the Raman spectrum were then identified through signal interpolation, signal deconvolution, and curve fitting. Statistical analysis was conducted on the extracted features to remove those with low statistical values, thus helping the machine learning algorithms to train the classifier models more efficiently. From this study, it was found that peaks at 1155 cm-1, 1525 cm-1, 1440 cm-1, and 1655 cm-1 from the Raman spectrum were highly correlated with the FFA levels of palm oils obtained through titration. The peak ratios of 1525 cm-1/1655 cm-1 and 1155 cm-1/1655cm-1 also showed a good and fast relationship for analyzing the FFA content of palm oil. © 2023 IEEE.

关键词： Curve fitting

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A Multimodal Contrastive Pre-training Framework for automatic Ship Detection in SAR Imagery 7

A Multimodal Contrastive Pre-training Framework for Automati...

引用

7th IEEE International conference on Transportation Information and Safety, ICTIS 2023

作者： Gong, Peizhu Liu, Jin Zhang, Xiliang Lin, Jianhua Wang, Junxiang Wu, Zhongdai Shanghai Maritime University College of Information Engineering Shanghai China National Engineering Research Center of Ship Transportation Control Systems Shanghai Ship and Shipping Research Institute Shanghai China National Engineering Research Center of Ship Transportation Control Systems Cosco Shipping Technology Co. Ltd Shanghai China State Key Laboratory of Maritime Technology and Safety Shanghai Ship and Shipping Research Institute Shanghai China

ISBN: (纸本)9798350308532

Synthetic aperture radar (SAR) imagery has been used as a promising data source for automatic ship detection with its capability of targeting objects in all-day and all-weather conditions. Due to the scarce labeled SAR images, previous research usually adopts optical data-based pre-training model to support SAR ship detectors and has made significant progress. Nonetheless, two fundamental issues persist: (i) SAR images suffer from serious intrinsic speckle noise with less texture features;(ii) Strong class-discriminative biases for imaging perspectives and geometry are introduced in the process of transferring pre-trained model. In this paper, we propose a multimodal contrastive pre-training framework (MCPF) to boost automatic ship detection with both intra- and cross-modal correlations between SAR and optical images. Specifically, MCPF implements multimodal contrastive learning strategy by introducing cross-modal contrastive module (CCM) and intra-modal contrastive module (ICM). CCM aims to pull the matched SAR-Optical pairs together while pushing those non-matched apart by measuring the similarity between latent representations. ICM maximizes agreement between differently augmented views of the same data example within each modality. To the best of our knowledge, MCPF is the first work that takes into account multimodal contrastive representation learning for automatic ship detection. Extensive experiments on benchmark datasets demonstrate that MCPF outperforms the existing methods and achieves the state-of-the-art performance. © 2023 IEEE.

关键词： Synthetic aperture radar

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Research on lightweight detection algorithm of Infrared Image based on YOLOv5 4

Research on lightweight detection algorithm of Infrared Imag...

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4th International conference on Advanced Algorithms and Signal Image processing, AASIP 2024

作者： Zhang, Wenyi Mo, Hongwei Wu, Songling Li, Xiaomin Harbin Engineering University School of Intelligent Science and Engineering Harbin150000 China Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing100854 China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

At present, infrared image can show good detection effect in complex environment because of its unique imaging characteristics, which brings a lot of convenience to people's production and life. However, because the infrared image contains a great lot of unnecessary information, it has great requirements for computing power and memory capacity, so it is challenging to satisfy the demands of real-time detection on resource-constrained portable mobile devices. To solve this problem, this paper proposes a refined lightweight algorithm built upon the YOLOv5, which changes the backbone network of the model to GhostNet network, introduces CA attention mechanism into the model to improve the detection effect of small targets, and changes the loss function of the model to Focal-EIoU loss function to improve the location effect of the boundary box and quicken the model's rate of convergence. Then replace the conventional convolution used in the sampling process of the model with the space-to-depth convolution module, in order to enhance the model's detection performance on low-resolution images and enable the extraction of more feature information. According to the experimental results, the enhanced detection algorithm is just 1% less than the initial YOLOv5 algorithm, and the model volume and floating point calculation are reduced by 31.9% and 36% respectively. This is better suited for deployments that are lightweight. © 2024 SPIE.

关键词： Convolution

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Modified U-Net Architeture based Ischemic Stroke Lesions Segmentation 21

Modified U-Net Architeture based Ischemic Stroke Lesions Seg...

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21st IEEE International conference on Sciences and Techniques of automatic control and Computer Engineering, STA 2022

作者： Abdmouleh, Nabil Echtioui, Amira Kallel, Fathi Hamida, Ahmed Ben Advanced Technologies for Medicine and Signals ENIS Sfax University ATMS Lab Sfax Tunisia National Engineering School of Electronics and Communications Sfax University Tunisia College of Computer Science King Khaled University 'KKU' Department Is Saudi Arabia

ISBN: (纸本)9781665482615

An ischemic stroke transpires when part of the brain tissue is unable to get oxygen and nutrients due to the interruption or the reduction of blood supply. A stroke is a medical emergency that can lead to serious complications if left untreated. Early action can reduce potential brain damage and other consequences. Therefore, quantitative assessment of ischemic stroke lesions using magnetic resonance imaging (MRI) is paramount for an accurate and decisive diagnosis. However, a diagnosis of hundreds of MRI-generated slices by trained specialists is time-consuming and requires painstaking attention to details. Automating this process using advanced image segmentation methods allows an exact localization of the tissue presenting lesions and provide an approximation of its volume. automatic detection of lesions provides faster answers and better assessments to assist medical practitioners. In this paper, we propose a novel method for ischemic stroke lesions detection based on a modified UNet architecture. We use the data from 2015 Ischemic Stroke Lesion Segmentation Challenge (ISLES 2015) for performance evaluation of our proposed method. Experiments have shown that the latter outperforms the state-of-the-art methods. © 2022 IEEE.

关键词： Magnetic resonance imaging

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M3D-NCA: Robust 3D Segmentation with Built-In Quality control 1

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26th International conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Kalkhof, John Mukhopadhyay, Anirban Tech Univ Darmstadt Karolinenpl 5 D-64289 Darmstadt Germany

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Medical image segmentation relies heavily on large-scale deep learning models, such as UNet-based architectures. However, the real-world utility of such models is limited by their high computational requirements, which makes them impractical for resource-constrained environments such as primary care facilities and conflict zones. Furthermore, shifts in the imaging domain can render these models ineffective and even compromise patient safety if such errors go undetected. To address these challenges, we propose M3D-NCA, a novel methodology that leverages Neural Cellular Automata (NCA) segmentation for 3D medical images using n-level patchification. Moreover, we exploit the variance in M3D-NCA to develop a novel quality metric which can automatically detect errors in the segmentation process of NCAs. M3D-NCA outperforms the two magnitudes larger UNet models in hippocampus and prostate segmentation by 2% Dice and can be run on a Raspberry Pi 4 Model B (2GB RAM). This highlights the potential of M3D-NCA as an effective and efficient alternative for medical image segmentation in resource-constrained environments.

关键词： Neural Cellular Automata Medical Image Segmentation automatic Quality control

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