检索结果-内蒙古大学图书馆

deep learning for Sensing Matrix Prediction in Computational Microwave Imaging With Coded-Apertures

IEEE ACCESS 2024年 12卷 16844-16855页

作者： Zhang, Jiaming Sharma, Rahul Garcia-Fernandez, Maria Alvarez-Narciandi, Guillermo Abbasi, Muhammad Ali Babar Yurduseven, Okan Queens Univ Belfast Ctr Wireless Innovat CWI Sch Elect Elect Engn & Comp Sci EEECS Belfast BT3 9DT North Ireland

This work aims to simplify the characterization process of coded-apertures for computational imaging (CI) at microwave frequencies. A major benefit of the presented technique is the minimization of the processing time needed to calculate the system sensing matrix for microwave CI-based compressive sensing applications. To achieve this, a deep learning-based approach which is capable of generating the sensing matrix using features learned directly from the coded-aperture distribution is proposed. To avoid the vanishing gradient problem, the proposed deep learning network contains skip connections. Using a dataset of 1,000 testing samples, the average normalized mean-squared-error (NMSE) calculated between the sensing matrix generated by the conventional method and that predicted by the proposed network is 0.0036. Moreover, the average mean-squared-error (MSE) calculated between the images reconstructed using the conventional and the predicted sensing matrix is 0.00297. In addition to providing high-fidelity estimations with minimized error, we demonstrate that using the trained network, the prediction of the sensing matrix can be achieved in 0.212 s, corresponding to a 65% reduction in the computation time needed to calculate the sensing matrix. This has significant outcomes in achieving real-time operation of CI-based microwave imaging systems.

关键词： Computational imaging deep learning image reconstruction microwave imaging sensing matrix

来源：评论

学校读者我要写书评

暂无评论

Fast halftoning policy network with error feedback

引用

JOURNAL OF ELECTRONIC IMAGING 2023年第4期32卷

作者： Jiang, Haitian Xiong, Dongliang Yin, Aiguo Ding, Li Huang, Kai Zhejiang Univ Inst VLSI Design Hangzhou Peoples R China Pantum Elect Zhuhai Peoples R China Apex Microelect Zhuhai Peoples R China

deep learning-based halftoning methods have demonstrated their abilities in dithering images with desired blue-noise properties. However, tons of parameters and computations come along with the 2D convolutional neural networks (CNNs), which hinder their practical usage, particularly in the cost-sensitive printing imaging system equipped with low-end computing devices. We devise a fast halftoning policy network to alleviate the problem. Motivated by classic error diffusion algorithms, we introduce an error feedback mechanism to model the dependencies between halftone lines. Consequently, the problem collapses to 1D deep halftoning within each line, which simplifies the learning task. Furthermore, a very simple convolutional layer is applied to capture the texture details from the continuous-tone image. We find that the proposed halftoning model can stably converge under the reinforcement learning-based framework. Experiments show that our model achieves a 10.6x speedup with only 1.7% parameters compared to a previous 2D CNN-based work while maintaining similar halftone quality. We have also conducted experiments on a digital signal processor to demonstrate how the proposed model saves expensive off-chip memory accesses and improves processing throughput in real scenarios.

关键词： halftones printing real-time imaging image processing

来源：评论

学校读者我要写书评

暂无评论

deep learning and Transfer learning in Cardiology: A Review of Cardiovascular Disease Prediction Models

引用

IEEE ACCESS 2024年 12卷 193365-193386页

作者： Sunilkumar, G. Kumaresan, P. Vellore Inst Technol Sch Comp Sci Engn & Informat Syst SCORE Vellore 632014 India

Cardiovascular disorders are the primary cause of death on a global scale. The World Health Organization report indicates that approximately 18 million people die from CVD each year. Major cardiac risks include arrhythmia and coronary artery disease, among others. Recent advancements in Artificial Intelligence play a pivotal role for life-saving interventions in CVD treatment. This survey examines the latest progress in Machine learning, deep learning and Pre-trained transfer learning models for classifying and predicting CVD using a survey of 159 articles, including 36 image datasets, 41 signal data and 52 clinical data from various sources. The survey investigates cardiac risk factors, cardiac dysfunction classification, various modalities and medical image processing techniques, performance metrics and hybrid techniques. Surveys on traditional neural networks such as Convolutional Neural Networks, Artificial Neural Networks, and Recurrent Neural Networks often achieve an accuracy rate of 70% to 95%. Leveraging pre-trained architectures such as ResNet, DenseNet, AlexNet, MobileNet, EfficientNet and GoogLeNet, transfer learning models consistently outperform other approaches frequently achieving accuracy levels greater than 96%. Researchers utilize various hybrid optimization algorithms to improve overall accuracy rate. The outcome of the survey supports a precise prognosis for patients with comorbidities. The survey findings indicate limitations in the incorporation of multimodal data for real-time risk assessment. The study results possess the capacity to bridge gaps in research on cardiovascular disease prediction, thereby assisting medical practitioners in early identification and subsequent prognosis.

关键词： Diseases Arteries Cardiovascular diseases Solid modeling Biomedical imaging Surveys deep learning Transfer learning Blood flow Blood Cardiovascular disease machine learning deep CNN transfer learning optimization hybrid model

来源：评论

学校读者我要写书评

暂无评论

Hybrid features extraction for the online mineral grades determination in the flotation froth using deep learning

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 129卷

作者： Bendaouia, Ahmed Abdelwahed, El Hassan Qassimi, Sara Boussetta, Abdelmalek Benzakour, Intissar Benhayoun, Abderrahmane Amar, Oumkeltoum Bourzeix, Francois Baina, Karim Cherkaoui, Mouhamed Hasidi, Oussama Cadi Ayyad Univ Fac Sci Semlalia Comp Sci Dept Comp Syst Engn Lab LISI Marrakech Morocco Moroccan Fdn Adv Sci Innovat & Res UM6P Rabat Morocco R&D & Engn Ctr Managem Grp Reminex Marrakech Morocco Cadi Ayyad Univ Fac Sci & Technol Comp & Syst Engn Lab L2IS Marrakech Morocco Mohammed V Univ Ecole Natl Super Informat Anal Syst ENSIAS Rabat 10000 Morocco ENSMR Engn Sch Rabat Morocco

The control of the froth flotation process in the mineral industry is a challenging task due to its multiple impacting parameters. Accurate and convenient examination of the concentrate grade is a crucial step in realizing effective and real-time control of the flotation process. The goal of this study is to employ image processing techniques and CNN-based features extraction combined with machine learning and deep learning to predict the elemental composition of minerals in the flotation froth. A real world dataset has been collected and preprocessed from a differential flotation circuit at the industrial flotation site based in Guemassa, Morocco. Using image-processing algorithms, the extracted features from the flotation froth include: the texture, the bubble size, the velocity and the color distribution. To predict the mineral concentrate grades, our study includes several supervised machine learning algorithms (ML), artificial neural networks (ANN) and convolutional neural networks (CNN). The industrial experimental evaluations revealed relevant performances with an accuracy up to 0.94. Furthermore, our proposed Hybrid method was evaluated in a real flotation process for the Zn, Pb, Fe and Cu concentrate grades, with an error of precision lesser than 4.53. These results demonstrate the significant potential of our proposed online analyzer as an artificial intelligence application in the field of complex polymetallic flotation circuits (Pb, Fe, Cu, Zn).

关键词： Machine learning deep learning Computer vision Features extraction Mining industry Industry 4.0 Flotation

来源：评论

学校读者我要写书评

暂无评论

Automatic detection of subsurface defects in concrete structures using state-of-the-art deep learning-based object detectors on the infrared dataset

引用

ENGINEERING STRUCTURES 2025年 329卷

作者： Ta, Quang Tai Mac, Van Ha Huh, Jungwon Yim, Hong Jae Lee, Giyeol Chonnam Natl Univ Dept Architecture & Civil Engn Gwangju 61186 South Korea Univ Transport & Commun Fac Civil Engn Dept Urban Transport & Marine Coastal Engn 3 Cau GiayLang Thuong Hanoi 100000 Vietnam Pusan Natl Univ Dept Civil Engn Busan 46241 South Korea Chonnam Natl Univ Dept Landscape Architecture Gwangju 61186 South Korea

Coupling infrared thermography (IRT) with deep learning is a potential approach to address the limitations of conventional IRT-based concrete delamination identification. However, it remains under-explored, mainly due to the scarcity of infrared data. This study examined numerous advanced object detectors, including Faster R-CNN, SSD, and the variants of YOLO versions (YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLO-NAS, and YOLOv9) to comprehensively evaluate their applicability on a self-prepared infrared dataset comprising 12,000 images of artificial defects. The results demonstrated that most of the selected models worked efficiently on the IRT dataset with precision, recall, F1-score, and AP50 greater than 91 % and real-time speeds higher than 45 FPS. The effectiveness of each model was elucidated, and the most suitable frameworks for delamination detection systems were proposed. Interestingly, newer versions of the selected YOLO models failed to surpass their predecessors. Moreover, larger variants did not outperform smaller ones within each YOLO version. Additionally, the effects of various IRT experimental conditions, image processing techniques, and data augmentation strategies on the model's performance were thoroughly investigated. Consequently, the capabilities and limitations of the automated approaches presented in this work form a solid foundation for further studies on automatically detecting internal defects in concrete structures using deep learning models and IRT inspection data.

关键词： Infrared thermography (IRT) Concrete structures Delamination deep learning Object detection Data augmentation YOLO

来源：评论

学校读者我要写书评

暂无评论

deep learning Method for real-time Fire Detection System for Urban Fire Monitoring and Control

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2024年第1期17卷 1-14页

作者： Yang, Wenyang Wu, Yesen Chow, Steven Kwok Keung Xian Shiyou Univ Sch Comp Xian 710065 Peoples R China South Australian Hlth & Med Res Inst Clin & Res Imaging Ctr Adelaide SA 5000 Australia

During urban fire incidents, real-time videos and images are vital for emergency responders and decision-makers, facilitating efficient decision-making and resource allocation in smart city fire monitoring systems. However, real-time videos and images require simple and embeddable models in small computer systems with highly accurate fire detection ratios. YOLOv5s has a relatively small model size and fast processing time with limited accuracy. The aim of this study is to propose a method that employs a YOLOv5s network with a squeeze-and-excitation module for image filtering and classification to meet the urgent need for rapid and accurate real-time screening of irrelevant data. In this study, over 3000 internet images were used for crawling and annotating to construct a dataset. Furthermore, the YOLOv5, YOLOv5x and YOLOv5s models were developed to train and test the dataset. Comparative analysis revealed that the proposed YOLOv5s model achieved 98.2% accuracy, 92.5% recall, and 95.4% average accuracy, with a remarkable processing speed of 0.009 s per image and 0.19 s for a 35 frames-per-second video. This surpasses the performance of other models, demonstrating the efficacy of the proposed YOLOv5s for real-time screening and classification in smart city fire monitoring systems.

关键词： Fire detection YOLOv5 network Squeeze-and-excitation module

来源：评论

学校读者我要写书评

暂无评论

Single image deraindrop leveraging luminance priors and context aggregation

引用

NEUROCOMPUTING 2024年 598卷

作者： Liu, Yi Gao, Zhi Mei, Tiancan Yi, Han Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430072 Peoples R China Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Natl Univ Singapore Sch Comp Singapore 117456 Singapore

Many deep learning -based raindrop removal methods have been proposed recently and demonstrated good performance. However, most deep learning based raindrop removal methods struggle to restore distorted color and texture resulting from the refraction of raindrops emitted from different background points. To overcome this challenge, we propose a novel recurrent single -image deraindrop approach that utilizes luminance priors and contextual feature aggregation. Our method decomposes the deraindrop process into a raindrop detection network and a raindrop removal network, leveraging the luminance differences between the raindrop and non -raindrop regions for accurate detection. Contextual feature aggregation, achieved by dilated convolutions with different rates, helps recover raindrop attachment areas and maintain consistency in color and texture. We have extensively evaluated our method on synthetic and real raindrop datasets, demonstrating its effectiveness, generalization capability, and superior performance compared to state-of-the-art methods.

关键词： Single image deraindrop Luminance prior Context aggregation Dilated convolution

来源：评论

学校读者我要写书评

暂无评论

2-D/3-D Medical image Registration Based on Feature-Point Matching

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷

作者： Si, Shengyuan Li, Zheng Lin, Ze Xu, Xian Zhang, Yudong Xie, Shipeng Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210003 Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Jiangsu Peoples R China

Two-dimensional/3-D medical image registration has a wide range of applications in intraoperative image-guided navigation, which can not only assist surgeons in accurately locating lesions but also serve as a key link for surgical robots to locate the surgical site. Current methods for 2-D/3-D spine image registration are susceptible to getting stuck in local optimization, struggling to extract gradient information from noisy real data, and exhibiting slow processing speeds. Recently, deep learning methods have suffered from insufficient training data, poor generalization performance, and a tendency to produce incorrect solutions. We propose an optimized model that significantly improves the speed and accuracy of 2-D/3-D registration by deeply integrating a feature-point matching network. This network demonstrates exceptional robustness in processing high-noise imagery and is adept at coarse registration, providing the initial solution for the optimized model and thereby abbreviating the time required for coarse registration. It also facilitates updates of parameter location modules within the optimized model, diminishing the overall computational demand. Additionally, by harnessing grayscale and spinal feature information, we formulate an objective function enriched with a feature-point similarity metric to govern the descent trajectory, culminating in heightened precision and expedited convergence. Our empirical findings indicate that this method achieves a mean accuracy of 0.2550 mm on real data, substantiating the efficacy of our approach.

关键词： Feature extraction Accuracy X-rays Linear programming Three-dimensional displays Telecommunications Surgery image registration deep learning Data models 2-D/3-D registration digital reconstruction radiography (DRR) feature-point matching network similarity measurement spine image registration

来源：评论

学校读者我要写书评

暂无评论

deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey

引用

SUSTAINABLE CITIES AND SOCIETY 2021年 65卷 102589页

作者： Bhattacharya, Sweta Maddikunta, Praveen Kumar Reddy Pham, Quoc-Viet Gadekallu, Thippa Reddy Krishnan, S. Siva Rama Chowdhary, Chiranji Lal Alazab, Mamoun Piran, Md. Jalil Vellore Inst Technol Sch Informat Technol & Engn Vellore Tamil Nadu India Pusan Natl Univ Res Inst Comp Informat & Commun Busan 46241 South Korea Charles Darwin Univ Coll Engn IT & Environm Casuarina NT Australia Sejong Univ Dept Comp Sci & Engn Seoul 05006 South Korea

Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities.

关键词： Artificial intelligence (AI) Big data Coronavirus pandemic COVID-19 Epidemic outbreak deep learning Medical image processing

来源：评论

学校读者我要写书评

暂无评论

YOLO9tr: a lightweight model for pavement damage detection utilizing a generalized efficient layer aggregation network and attention mechanism

引用

JOURNAL OF real-time image processing 2024年第5期21卷 1-13页

作者： Youwai, Sompote Chaiyaphat, Achitaphon Chaipetch, Pawarotorn King Mongkuts Univ Technol Thonburi Dept Civil Engn AI Res Grp Bangkok Thailand Infraplus Co Ltd Bangkok Thailand

Maintaining road pavement integrity is crucial for ensuring safe and efficient transportation. Conventional methods for assessing pavement condition are often laborious and susceptible to human error. This paper proposes YOLO9tr, a novel lightweight object detection model for pavement damage detection, leveraging the advancements of deep learning. YOLO9tr is based on the YOLOv9 architecture, incorporating a partial attention block that enhances feature extraction and attention mechanisms, leading to improved detection performance in complex scenarios. The model is trained on a comprehensive dataset comprising road damage images from multiple countries. This dataset includes an expanded set of damage categories beyond the standard four types (longitudinal cracks, transverse cracks, alligator cracks, and potholes), providing a more nuanced classification of road damage. This broadened classification range allows for a more accurate and realistic assessment of pavement conditions. Comparative analysis demonstrates YOLO9tr's superior precision and inference speed compared to state-of-the-art models like YOLOv8, YOLOv9 and YOLOv10, achieving a balance between computational efficiency and detection accuracy. The model achieves a high frame rate of up to 136 FPS, making it suitable for real-time applications such as video surveillance and automated inspection systems. The research presents an ablation study to analyze the impact of architectural modifications and hyperparameter variations on model performance, further validating the effectiveness of the partial attention block. The results highlight YOLO9tr's potential for practical deployment in real-time pavement condition monitoring, contributing to the development of robust and efficient solutions for maintaining safe and functional road infrastructure.

关键词： YOLO Pavement damage Object detection deep learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：