检索结果-内蒙古大学图书馆

real-time disease detection on bean leaves from a small image dataset using data augmentation and deep learning methods: real-time disease detection on bean leaves from a small image dataset using data augmentation..: E. Karantoumanis et al.

引用

Soft Computing 2024年第21期28卷 12929-12941页

作者： Karantoumanis, Emmanouil Balafas, Vasileios Louta, Malamati Ploskas, Nikolaos Department of Electrical and Computer Engineering University of Western Macedonia Campus ZEP Kozani50100 Greece

Disease detection in agricultural crops plays a pivotal role in ensuring food security and sustainable farming practices. deep learning models, known for their ability in image analysis, often demand extensive image datasets and annotations to achieve high accuracy. However, in the case of bean crops, the absence of a publicly available dataset has posed a significant challenge for applying deep learning algorithms to accurately predict diseases. Additionally, the manual annotation of images demands substantial time and resources. This paper introduces an innovative approach to tackle these issues. We introduce a solution for real-time disease detection on bean leaves, despite the lack of bean-specific image data. Initially, we generate a small dataset from real images and annotate them. Then, we utilize images from the existing dataset PlantDoc (Singh et al. in: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, Association for Computing Machinery, pp 249–253, 2020) from leaves of other plant species. Moreover, to compensate for the limitations of a small image dataset, we employ advanced data augmentation techniques, enriching the training data and enhancing the model’s ability to generalize. Our experimental study shows that data augmentation techniques can improve the accuracy of deep learning methods by up to 37%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Activity Monitoring for ICU Patients Using deep learning and image processing

引用

SN Computer Science 2020年第3期1卷 1-8页

作者： Magi, Nikitaa Prasad, B.G. Department of Computer Science and Engineering B. M. S. College of Engineering Bull Temple Road Basavanagudi Bangalore 560019 India

A patient in coma condition needs intense monitoring and care, but nowadays shortage of intensivist and critical care nurses is the major problem faced by the hospitals. Traditional method of monitoring ICU patients requires dedicated proactive observer to look at the patient all day long. One of the issues with traditional methods is that the observer has to be available all 24 h of the day and one observer will be able to monitor only one patient at a time. This paper aims to develop a system which overcomes these issues faced by traditional methods. The proposed system uses image processing and deep learning approach, with which the system can monitor the patients in critical care unit and can report to the doctor or nurse. The proposed system is capable of detecting target object (patient) in real time, further detect patient’s activity if any and notify the doctor or nurse. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence deep learning Human activity analysis image processing Neural network Object detection Patient monitoring

来源：评论

学校读者我要写书评

暂无评论

Classification of real and bogus transients using active learning and semi-supervised learning

引用

ASTRONOMY & ASTROPHYSICS 2025年 693卷

作者： Liu, Yating Fan, Lulu Hu, Lei Lu, Junqiang Lu, Yan Xu, Zelin Zhu, Jiazheng Wang, Haochen Kong, Xu Univ Sci & Technol China Sch Artificial Intelligence & Data Sci Hefei 230026 Peoples R China Univ Sci & Technol China Dept Astron CAS Key Lab Res Galaxies & Cosmol Hefei 230026 Peoples R China Univ Sci & Technol China Sch Astron & Space Sci Hefei 230026 Peoples R China Deep Space Explorat Lab Hefei 230088 Peoples R China Carnegie Mellon Univ McWilliams Ctr Cosmol Dept Phys 5000 Forbes Ave Pittsburgh PA 15213 USA Shanghai AI Lab Shanghai 200232 Peoples R China Purple Mt Observ Nanjing 210023 Peoples R China

Context. The mounting data stream of large time-domain surveys renders the visual inspections of a huge set of transient candidates impractical. Techniques based on deep learning-based are popular solutions for minimizing human intervention in the time domain community. The classification of real and bogus transients is a fundamental component in real-time data processing systems and is critical to enabling rapid follow-up observations. Most existing methods (supervised learning) require sufficiently large training samples with corresponding labels, which involve costly human labeling and are challenging in the early stages of a time-domain survey. One method that can make use of training samples with access to only a limited amount of labels is highly desirable for future large time-domain surveys. These include the forthcoming 2.5-meter Wide-Field Survey Telescope (WFST) six-year survey and the Vera C. Rubin Observatory Legacy Survey of Space and time (LSST). Aims. deep-learning-based methods have been favored in astrophysics owing to their adaptability and remarkable performance. They have been applied to the task of the classification of real and bogus transients. Unlike most existing approaches, which necessitate massive and expensive annotated data, we aim to leverage training samples with only 1000 labels and discover real sources that vary in brightness over time in the early stages of the WFST six-year survey. Methods. We present a novel deep learning method that combines active learning and semi-supervised learning to construct a competitive real-bogus classifier. Our method incorporates an active learning stage, where we actively select the most informative or uncertain samples for annotation. This stage aims to achieve higher model performance by leveraging fewer labeled samples, thus reducing annotation costs and improving the overall learning process efficiency. Furthermore, our approach involves a semi-supervised learning stage that exploits the unlab

关键词： techniques: image processing

来源：评论

学校读者我要写书评

暂无评论

Integrating YOLOv8 and CSPBottleneck based CNN for enhanced license plate character recognition

引用

JOURNAL OF real-time image processing 2024年第5期21卷 168页

作者： Khokhar, Sahil Kedia, deepak GJUS&T Dept ECE Hisar India

The paper introduces an integrated methodology for license plate character recognition, combining YOLOv8 for segmentation and a CSPBottleneck-based CNN classifier for character recognition. The proposed approach incorporates pre-processing techniques to enhance the recognition of partial plates and augmentation methods to address challenges arising from colour diversity. Performance analysis demonstrates YOLOv8's high segmentation accuracy and fast processing time, complemented by precise character recognition and efficient processing by the CNN classifier. The integrated system achieves an overall accuracy of 99.02% with a total processing time of 9.9 ms, offering a robust solution for automated license plate recognition (ALPR) systems. The integrated approach presented in the paper holds promise for the practical implementation of ALPR technology and further development in the field of license plate recognition systems.

关键词： Computer vision Optical character recognition deep learning Artificial intelligence ALPR

来源：评论

学校读者我要写书评

暂无评论

real-time diabetic foot ulcer classification based on deep learning & parallel hardware computational tools

引用

MULtimeDIA TOOLS AND APPLICATIONS 2024年第27期83卷 70369-70394页

作者： Fadhel, Mohammed A. Alzubaidi, Laith Gu, Yuantong Santamaria, Jose Duan, Ye Univ Sumer Coll Comp Sci & Informat Technol Thi Qar 64005 Iraq Queensland Univ Technol Sch Mech Med & Proc Engn Brisbane Qld 4000 Australia Queensland Univ Technol ARC Ind Transformat Training Ctr Joint Biomech Brisbane Qld 4000 Australia Akunah Med Technol Pty Ltd Co Brisbane Qld 4120 Australia Univ Jaen Dept Comp Sci Jaen 23071 Spain Clemson Univ Sch Comp Clemson SC 29631 USA

Meeting the rising global demand for healthcare diagnostic tools is crucial, especially with a shortage of medical professionals. This issue has increased interest in utilizing deep learning (DL) and telemedicine technologies. DL, a branch of artificial intelligence, has progressed due to advancements in digital technology and data availability and has proven to be effective in solving previously challenging learning problems. Convolutional neural networks (CNNs) show potential in image detection and recognition, particularly in healthcare applications. However, due to their resource-intensiveness, they surpass the capabilities of general-purpose CPUs. Therefore, hardware accelerators such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and graphics processing units (GPUs) have been developed. With their parallelism efficiency and energy-saving capabilities, FPGAs have gained popularity for DL networks. This research aims to automate the classification of normal and abnormal (specifically Diabetic Foot Ulcer-DFU) classes using various parallel hardware accelerators. The study introduces two CNN models, namely DFU_FNet and DFU_TFNet. DFU_FNet is a simple model that extracts features used to train classifiers like SVM and KNN. On the other hand, DFU_TFNet is a deeper model that employs transfer learning to test hardware efficiency on both shallow and deep models. DFU_TFNet has outperformed AlexNet, VGG16, and GoogleNet benchmarks with an accuracy 99.81%, precision 99.38% and F1-Score 99.25%. In addition, the study evaluated two high-performance computing platforms, GPUs and FPGAs, for real-time system requirements. The comparison of processing time and power consumption revealed that while GPUs outpace FPGAs in processing speed, FPGAs exhibit significantly lower power consumption than GPUs.

关键词： deep learning Double transfer learning FPGA GPU DFU real-time Parallel hardware Medical imaging

来源：评论

学校读者我要写书评

暂无评论

deep learning-Based Optimization of Cloud Enterprise Resource Planning (ERP) Systems for Adaptive Decision Support and Management Effectiveness Analysis

引用

IEEE ACCESS 2024年 12卷 193402-193415页

作者： Zhang, Li-Sen George Washington Univ Sch Business Washington DC 20052 USA

Modern enterprise resource planning (ERP) systems face the challenge of handling massive amounts of data and supporting real-time decision-making. With the rapid changes in the market environment, traditional ERP systems are limited in their ability to make adaptive decisions. This study aims to address this issue by integrating deep learning techniques to enhance the management effectiveness of ERP systems. The study uses RNNs, CNNs and DRL models for time series prediction, image recognition and resource optimisation, respectively. The experimental results show that RNN achieves 95% accuracy in demand forecasting, CNN 98% accuracy in image recognition, and DRL achieves more than 10% cost savings in resource optimisation. The integrated ERP system achieved a 42.86% reduction in order processing time, a 25% improvement in inventory turnover, an 8% reduction in operating costs, and a 15% improvement in employee satisfaction. This study demonstrates the effectiveness of deep learning to enhance decision support in ERP systems and provides suggestions for future directions of improvement.

关键词： Optimization Accuracy Convolutional neural networks Adaptive systems Decision making Business Recurrent neural networks Predictive models Enterprise resource planning Adaptation models Thesaurus deep learning cloud-based enterprise resource planning system adaptive decision support management effectiveness

来源：评论

学校读者我要写书评

暂无评论

Perceptible Lightweight Zero-Mean Normalized Cross-Correlation for Infrared Template Matching

引用

IEEE ACCESS 2024年 12卷 164777-164791页

作者： Lee, Seungeon Kim, Donyung Park, Inho Kim, Geonjong Kim, Sungho Yeungnam Univ Dept Elect Engn Gyongsan 38541 Gyeongsangbug D South Korea Hanwha Syst Seoul 04541 South Korea

Infrared template matching is an essential technology that enables reliable and accurate object detection, recognition, and tracking in complex environments. Perceptible Lightweight Zero-mean normalized cross-correlation (ZNCC) Template Matching (PLZ-TM) has been proposed as a tool for matching infrared images obtained from cameras with different fields of view. Aligning such images is challenging because of the involved differences in thermal distributions, focus discrepancies, background elements, and distortions. The first stage of PLZ-TM involves extracting feature maps from the search and template images using a deep learning network. This deep learning network is designed with a Convolutional Neural Network (CNN) architecture that omits pooling layers, thereby minimizing information loss during extraction. The subsequent stage involves matching the feature maps. The matching method utilizes a lightweight ZNCC (ZNCC) module that employs average pooling for training. The deep learning network is trained to optimize the distribution of the output heatmap and the probability at the correct location of the template image. PLZ-TM delivers excellent performance achieving a processing time of only 3.3 ms in matching a $640\times 480$ search image with a $192\times 144$ template image. Moreover, it attains a matching accuracy of 96% on a dataset obtained from infrared cameras with different fields of view.

关键词： Feature extraction deep learning image matching Accuracy Training Cameras real-time systems Brightness Object tracking Convolutional neural networks Infrared imaging Template matching zero-mean normalized cross correlation real-time infrared image matching convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Flame lift-off detector based on deep learning neural networks

引用

COMBUSTION AND FLAME 2024年 260卷

作者： Gharib, Mohsen Tischer, Paul Schulze, Olaf Graebner, Martin Richter, Andreas Fraunhofer Inst Ceram Technol & Syst Winterbergstr 28 D-01277 Dresden Germany TU Bergakad Freiberg Inst Energy Proc Engn & Chem Engn Fuechsmuehlenweg 9 D-09599 Freiberg Germany

This study evaluates the application of deep learning techniques in real-time flame lift-off detection and lift -off length prediction from flame images. A multi-feed test facility equipped with an optical diagnostic system was used to investigate the flame stability of both liquid and solid fuels. A high-speed camera was used to capture flame images, and image processing techniques were employed to extract the data. In the case of flame detection from images captured by high-speed cameras, the temporal resolution of the images is very high, which is beneficial for detecting rapid changes in the flame dynamics. In the current study, the analysis of 10,000 images by images processing methods takes approximately 60 min. Therefore, traditional methods may not be suitable for real-time monitoring, as they may not be able to capture the detailed variations in the flame appearance and intensity. The objective of this study was to find a fast and computationally efficient way to detect flame lift-off in high-speed online flame monitoring system. Two models were used in this study: a convolutional autoencoder and a regression neural network. The results demonstrate the potential of deep learning techniques in online flame diagnostics. The proposed approach provides a fast and efficient way to process 10,000 images in only 3.5 s, making it suitable for implementation in real-time flame monitoring *** and significancePrevious studies in the field of real-time flame diagnostics have predominantly used low sampling rates to capture flame images. However, when high-speed cameras are used, conventional image processing techniques prove inadequate to provide instant results for real-time monitoring systems. This study presents a novel deep learning approach capable of directly detecting flame lift-off from high-speed images without the need for separate segmentation, thereby significantly enhancing the analysis speed. The results obtained in this paper emphasize the ad

关键词： Flame detection Flame lift-off Neural networks Flame stability Gasification Partial oxidation

来源：评论

学校读者我要写书评

暂无评论

An end-to-end deep learning approach for real-time single image dehazing

引用

JOURNAL OF real-time image processing 2023年第1期20卷 12页

作者： Jeong, Chi Yoon Moon, KyeongDeok Kim, Mooseop Elect & Telecommun Res Inst Artificial Intelligence Res Lab 218 Gajeong Ro Daejeon 34129 South Korea Univ Sci & Technol Artificial Intelligence 217 Gajeong Ro Daejeon 34113 South Korea

image dehazing methods can restore clean images from hazy images and are popularly used as a preprocessing step to improve performance in various image analysis tasks. In recent times, deep learning-based methods have been used to sharply increase the visual quality of restored images, but they require a long computation time. The processing time of image-dehazing methods is one of the important factors to be considered in order not to affect the latency of the main image analysis tasks such as detection and segmentation. We propose an end-to-end network model for real-time image dehazing. We devised a zoomed convolution group that processes computation-intensive operations with low resolution to decrease the processing time of the network model without performance degradation. Additionally, the zoomed convolution group adopts an efficient channel attention module to improve the performance of the network model. Thus, we designed a network model using a zoomed convolution group to progressively recover haze-free images using a coarse-to-fine strategy. By adjusting the sampling ratio and the number of convolution blocks that make up the convolution group, we distributed small and large computational complexities respectively in the early and later operational stages. The experimental results with the proposed method on a public dataset showed a real-time performance comparable to that of another state-of-the-art (SOTA) method. The proposed network's peak-signal-to-noise ratio was 0.8 dB lower than that of the SOTA method, but the processing speed was 10.4 times faster.

关键词： Single-image dehazing Lightweight dehazing networks image restoration End-to-end learning Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

The use of intelligent real-time image acquisition system using data mining technology in news acquisition and editing practice

引用

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第2期24卷 639-656页

作者： Ma, Xiaowen Shandong Univ Arts Lib Jinan Shandong Peoples R China

Aiming to address the timely dissemination of news information, this work explores the clever utilization of data mining (DM) technology and deep learning (DL) algorithms to construct an intelligent real-time news image acquisition system to meet the urgency of news dissemination needs. First, this work introduces an intelligent real-time news image acquisition system and provides a detailed analysis of its principles and advantages. Throughout this process, the crucial role of DM technology in news image classification and automation is emphasized, especially in dealing with rapidly evolving news events. Next, the work establishes an intelligent real-time news image acquisition model based on DL algorithms, which integrates the essence of DM technology. Through this fusion, the research objective is to enhance the performance of the news image acquisition system to achieve higher real-time and accuracy, which is vital for the swift delivery of news information. Finally, this work investigates the application of the intelligent news image acquisition system in network communication to ensure its adaptability to various network communication scenarios while maintaining accuracy and real-time capabilities. The research results demonstrate that the application of DM technology in combination with DL algorithms can effectively meet the practical needs of the news industry, enhancing the automation of news image processing and enabling faster information delivery to the audience. Notably, the AlexNet model employed performs exceptionally well, achieving recognition rates of up to 99.6% after data augmentation or equalization processing, with an accuracy of 90.9% and a high specificity of 93.38%. This indicates outstanding overall classification accuracy and negative class accuracy, even when distinguishing between news and non-news scenarios. These results clearly underscore the connection between DM technology and news acquisition and editing practices, and emphasize it

关键词： deep learning data mining real-time image acquisition network security AlexNet model

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：