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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

共 14690 条记录，以下是241-250 订阅

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Fault Diagnosis of Wind Turbine Bearings Using Siamese Networks 19

Fault Diagnosis of Wind Turbine Bearings Using Siamese Netwo...

引用

IEEE 19th conference on Industrial Electronics and Applications (ICIEA)

作者： Lee, Injae Cho, HaeJun Kim, Kyungseok Lee, Hyunmin Kim, Jungchan Paik, Joonki Chung Ang Univ Dept Artificial Intelligence Seoul South Korea Chung Ang Univ Dept Image Seoul South Korea Doosan Enebil Turbomachinery Technol Dev Team Seoul South Korea

ISBN: (纸本)9798350360875;9798350360868

Bearing faults pose a significant threat to the reliability and efficiency of rotating machinery systems, particularly in wind turbines. Traditional fault diagnosis algorithms often face limitations, such as processing single signal inputs from each sensor without considering interferences between components. To overcome these shortcomings and leverage the potential of processing multi-dimensional signal inputs, we propose SiamFD-net, a deep learning-based method. Focusing on wind turbine bearing fault diagnosis, our model employs a Siamese network architecture, which distinguishes between similar and dissimilar input pairs. This approach not only addresses the limitations of traditional algorithms but also contributes to mitigating the class imbalance problem commonly encountered in fault diagnosis. Experimental results using the Doosan Wind Turbine bearing dataset demonstrate the comprehensive view and diagnosis capability of our model. Furthermore, we validate the effectiveness of SiamFDnet using the Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset. Our code is openly accessible at: https://***/SiamFDnet.

关键词： bearing fault diagnosis deep learning convolutional neural network siamese network

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Enhancing Structural Health Monitoring: AI-Driven image processing for Automated Crack Identification in Concrete Surfaces 5

Enhancing Structural Health Monitoring: AI-Driven Image Proc...

引用

5th International conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Jency, S. Ramkumar, G. Saveetha School of Engineering SIMATS Chennai Technology Department of Ece Chennai India

ISBN: (纸本)9798350379945

One of the first signs of structural deterioration is cracks in the concrete surface, which is important for maintenance because prolonged exposure will seriously harm the environment. The highly regarded method for inspecting cracks is manual inspection. During the handwritten inspection, the irregularities' conditions are noted and a hand-drawn illustrate of the crack is created. One of the main problems on large construction sites is the degradation of structures as a result of cracks, especially in cases where inspection by hand is not feasible. Thus, an alternative called automatic image-based crack detection is suggested. The primary goal of this research work is to use artificial intelligence-based image processing techniques to identify and categorize cracks. Here, this study proposes five modules. The first module collects the dataset of crack images;the second module applies a pre-processing method to filter the data;the third module divides the data into training and testing portions;and the fourth module implements a model that uses both deep learning, such as the Mobile-net algorithm, and machine learning, such as the Random Forest algorithm, for testing and training the data. The last part is the prediction module, which forecasts whether a crack image will be positive or negative. Lastly, this study compares the accuracy score, recall, precision, loss graph, F1 score value, and algorithms for machine learning along with deep learning. The system performed better, as demonstrated by the experimental results. © 2024 IEEE.

关键词： Random forests

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Temporal Deep Learning image processing Model for Natural Gas Leak Detection Using OGI Camera

Temporal Deep Learning Image Processing Model for Natural Ga...

引用

2024 Offshore Technology conference Asia, OTCA 2024

作者： Korjani, Mehdi Conley, David Smith, Mark Clean Connect Inc DenverCO United States

ISBN: (纸本)9781959025030

Natural gas extraction systems often encounter manufacturing defects or develop defects over time, leading to gas leaks. These leaks pose challenges, causing revenue losses and environmental pollution. Detecting gas leaks in the vast array of extraction, transfer, and storage equipment within these systems can be arduous, allowing leaks to persist unnoticed. Additionally, natural gas leaks are not visible to naked eyes, further complicating their detection. We developed a novel deep learning image processing model that utilizes videos captured by a specialized Optical Gas Imaging (OGI) camera to detect natural gas leaks. The temporal deep learning algorithm is designed to identify patterns associated with gas leaks and improve its performance through supervised learning. Our model incorporates algorithms to detect background environments, motion, equipment, and classify gas leaks. Our model employs leak identification algorithms to determine the presence of gas leaks. These algorithms calculate the probability of detected motion indicating a gas leak based on long-term and short-term background subtraction, detected motion, motion duration, equipment location, and telemetry data. To minimize false positives, we have developed image segmentation and object detection models to identify known objects, such as equipment, people, and cars, within the video footage. To train our model we collect more than 10,000 short videos from real fields and include simulated data with known rate controlled gas release in different situations. Data consist of wide range of weather situations including different temperature, wind speed, humidity in sunny, rainy, and snowy fields. We validated our model by conducting experiments involving actual footage from the field. The model achieved a 98% true positive rate, and a 100% true negative rate, correctly refraining from sending an alarm for all non-releases. Additionally, we developed a postprocessing algorithm capable of estimating the

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Automatic summarization of Instagram social network posts by combining semantic and statistical approaches 6

Automatic summarization of Instagram social network posts by...

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6th International conference on Pattern Recognition and image Analysis (IPRIA)

作者： Tabanmehr, Zainab Akhtarkavan, Ehsan Khatam Univ Dept Comp Engn Tehran Iran

ISBN: (纸本)9798350314557

The increasing spread of data and text documents such as articles, web pages, books, posts on social networks, etc. on the Internet, creates a fundamental challenge in various fields of text processing under the title of "automatic text summarization". Manual processing and summarization of large volumes of textual data is a very difficult, expensive, time-consuming, and impossible process for human users. Text summarization systems are divided into extractive and abstract categories. In the extractive summarization method, the final summary of a text document is extracted from the important sentences of the same document without any kind of change. In this method, it is possible to repeat a series of sentences repeatedly and interfere with pronouns. But in the abstract summarization method, the final summary of a textual document is extracted from the meaning of the sentences and words of the same document or other documents. Many of the performed works have used extraction methods or abstracts to summarize the collection of web documents, each of which has advantages and disadvantages in the results obtained in terms of similarity or size. In this research, by developing a crawler, extracting the popular text posts from the Instagram social network, suitable pre-processing, and combining the set of extractive and abstract algorithms, the researcher showed how to use each of the abstract algorithms. and used extraction as a supplement to increase the accuracy and accuracy of another algorithm. Observations made on 820 popular text posts on the Instagram social network show the accuracy (80%) of the proposed system.

关键词： text summarization extractive approach abstract approach natural language processing social networks

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An Effective Threshold Based Technique for Retinal image Blood Vessel Segmentation on Fundus image Using Average and Gaussian Filters 2nd

An Effective Threshold Based Technique for Retinal Image Blo...

引用

2nd International conference on Computing, Communication, and Learning, CoCoLe 2023

作者： Ray, Rahul Jena, Sudarson Parida, Priyadarsan SUIIT Sambalpur University Odisha Burla768018 India SUIIT Sambalpur University Odisha Burla768018 India GIET University Odisha Gunupur765022 India

ISBN: (纸本)9783031569975

The fundamental components of automated retinal blood vessel segmentation for eye disease screening systems are segmentation algorithms, retinal blood vessel datasets, classification algorithms, performance measure parameters and noise removing filters etc. This paper is presenting retinal blood vessel segmentation techniques on 2D retinal images captured by fundus camera and examining the performance of different algorithms for a better and accurate result. The goal of this study is to comprehensively investigate and assess retinal blood vessel extraction algorithms using publicly accessible High-Resolution Fundus Diabetic Retinopathy (HRF-DR) and Iowa Normative Set for processing images of the retina-artery vein ratio (INSPIRE-AVR) datasets, with a focus on important performance measurements and characteristics. The effectiveness of the proposed methodology is measured using the performance metrics like Sensitivity, Specificity and Accuracy with 94.27%, 87.86% and 95.61% for HRF-DR dataset and 91.10%, 86.97% and 95.16% for INSPIRE-AVR dataset respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image segmentation

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Comparative Study of image processing Neural Networks using Face Mask Dataset 1

Comparative Study of Image Processing Neural Networks using ...

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1st IEEE International conference on Advances in Electronics, Communication, Computing and Intelligent Information systems, ICAECIS 2023

作者： Sunny Jindal, Rajni Delhi Technological University Department of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350348057

Over the years there has been huge improvements in the performance of image processing algorithms due to increase in computation power of Devices as well as use of Neural Networks. This paper focuses on comparison of size and performance of different image Classification networks used widely. Each one of the machine learning models studied presents a new technique to process the input images that might be useful in different scenarios. Our main focus is to identify the framework with low latency, high accuracy as they can be used practically in surveillance equipment's that need to be fast and accurate. © 2023 IEEE.

关键词： Deep neural networks

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Realtime Scene Enhancement of Low Light image/Video 5

Realtime Scene Enhancement of Low Light Image/Video

引用

5th International conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Zeng, Huixia Xiao, Ping School of Electronic and Information Engineering Guangzhou City University of Technology Guangdong Guangzhou510800 China

ISBN: (纸本)9798350368284

The quality of image and videos plays a vital role in case of real-Time systems. images are captured without sufficient illumination, lead to low dynamic range and high propensity for generating high noise levels. The processing ideas and networks of several mainstream low-light image enhancement neural network algorithms such as DRBN, DSLR, ZeroDCE, EnlightenGAN, as well as the key technologies used are compared and analyzed. In this paper, the EnglightenGAN neural network is used as the core image enhancement algorithm, and NVIDIA AGX Xavier equipment and video stream processing technology are used to achieve the entire process of video acquisition, video frame encoding, image enhancement processing, frame decoding, and video stream display. It can adapt to most low-light and dark scenes which has a good enhancement effect on video streams collected in low-light scenes. The real-Time performance and image enhancement effect of the system achieved good results, with FPS reaching 10, SSIM reaching 0.51753, and PSNR reaching 27.89973. © 2024 IEEE.

关键词： Video streaming

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Research on Machine Vision Intelligent Detection System Based on Faster R-CNN 4

Research on Machine Vision Intelligent Detection System Base...

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4th IEEE International conference on Data Science and Computer Application, ICDSCA 2024

作者： Wang, Xueting Zeng, Fanshuo Song, Weizhao Zhang, Zhipeng He, Huitong Zhou, Zheyuan Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350368239

With the rapid development of artificial intelligence technology, visual inspection and image processing algorithms have been continuously improved in accuracy and efficiency, and intelligent inspection systems based on machine vision have been widely used in product quality control, defect detection and process monitoring. In this paper, a machine vision intelligent inspection system based on Faster Region-based Convolutional Network (Faster R-CNN) is proposed, which aims to improve the detection accuracy and efficiency. In this paper, the overall architecture of the system is introduced, including the image acquisition module, the data preprocessing module and the detection module based on Faster R-CNN. In the data preprocessing module, the Deep Convolution Generative Adversarial Networks (DCGAN) algorithm was proposed for image expansion generation, and high-resolution image samples that were basically consistent with the collected image datasets were obtained. Then, the Faster R-CNN model was optimized and adjusted for specific application scenarios, so that the average accuracy (mAP) of the model reached 78.23% and the FPS value reached 11.0. ©2024 IEEE.

关键词： Faster R-CNN image processing image recognition Machine vision intelligent inspection system

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Research on image Sharpness Evaluation in Microstructure processing

Research on Image Sharpness Evaluation in Microstructure Pro...

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2024 IEEE International conference on Manipulation, Manufacturing and Measurement on the Nanoscale, 3M-NANO 2024

作者： Zhang, Jiajun Wang, Zhe Li, Yiquan Xu, Jinkai Changchun University of Science and Technology Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing College of Mechanical and Electrical Engineering China Changchun University of Science and Technology School of Life Science and Technology China

ISBN: (纸本)9798350362107

To address issues of insufficient sensitivity and weak anti-noise characteristics in existing image sharpness evaluation algorithms, we propose a method combining local variance and gradient analysis. Traditional methods like the Tenengrad function use horizontal and vertical gradient values, while multi-directional gradients can increase noise sensitivity and false detections. Our approach integrates the Tenengrad function with gray variance processing, analyzing the deviation of each pixel's brightness from the average to distinguish content from background. Normalizing these values helps reduce background and impurity pixel influence. Preprocessing with an anisotropic diffusion filter smooths the image while preserving edges. Experimental results show this method offers higher sensitivity and better noise resistance than traditional functions, suitable for autofocus systems. © 2024 IEEE.

关键词： Anisotropy

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IMDL-BenCo: A Comprehensive Benchmark and Codebase for image Manipulation Detection & Localization 38

IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Ma, Xiaochen Zhu, Xuekang Su, Lei Du, Bo Jiang, Zhuohang Tong, Bingkui Lei, Zeyu Yang, Xinyu Pun, Chi-Man Lv, Jiancheng Zhou, Jizhe College of Computer Science Sichuan University China Department of Computer and Information Science University of Macao China Engineering Research Center of Machine Learning and Industry Intelligence MOE China

A comprehensive benchmark is yet to be established in the image Manipulation Detection & Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely undermining the development of this field. However, the scarcity of open-sourced baseline models and inconsistent training and evaluation protocols make conducting rigorous experiments and faithful comparisons among IMDL models challenging. To address these challenges, we introduce IMDL-BenCo, the first comprehensive IMDL benchmark and modular codebase. IMDL-BenCo: i) decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility;ii) fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark;and iii) conducts deep analysis based on the established benchmark and codebase, offering new insights into IMDL model architecture, dataset characteristics, and evaluation standards. Specifically, IMDL-BenCo includes common processing algorithms, 8 state-of-the-art IMDL models (1 of which are reproduced from scratch), 2 sets of standard training and evaluation protocols, 15 GPU-accelerated evaluation metrics, and 3 kinds of robustness evaluation. This benchmark and codebase represent a significant leap forward in calibrating the current progress in the IMDL field and inspiring future breakthroughs. Code is available at: https://***/scu-zjz/IMDLBenCo. © 2024 Neural information processing systems foundation. All rights reserved.

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