检索结果-内蒙古大学图书馆

A lightweight deep learning method for real-time weld feature extraction under strong noise

SIGNAL image AND VIDEO processing 2024年第11期18卷 8169-8184页

作者： Cheng, Jiaming Jin, Hui Southeast Univ Sch Civil Engn Jiangsu Key Lab Mech Anal Infrastructure & Adv Equ Nanjing Peoples R China

This paper proposes a lightweight deep learning (DL) framework for real-time accurate weld feature extraction from noisy images with light, smoke, or splash. Leveraging a two-dimensional human pose estimation paradigm, the framework follows a top-down architecture for accurate weld feature point localization. This study develops a semi-automatic annotation technique to dramatically reduce the annotation cost. Then, we design a lightweight yet faster You Only Look Once version 8 (YOLOv8) detector to rapidly detect the weld feature region in the presence of strong noise. To avoid reliance on high-resolution feature maps and achieve sub-pixel-level localization accuracy, a heatmap-free approach decomposes the feature point detection task into subtasks of horizontal and vertical coordinate classification. Comparison with mainstream DL-based weld recognition methods validates the superiority of the proposed method regarding real-time feature extraction accuracy and robustness.

关键词： Laser vision Pose estimation Weld feature extraction Coordinate classification YOLO

来源：评论

学校读者我要写书评

暂无评论

Enhancing fitness action recognition with ResNet-TransFit: Integrating IoT and deep learning techniques for real-time monitoring

引用

ALEXANDRIA ENGINEERING JOURNAL 2024年 109卷 89-101页

作者： Li, Jijie Gong, Ruyao Wang, Gang Kunsan Natl Univ Phys Educ Gunsan 54150 Jeollabuk South Korea Linyi Univ Coll Sports & Hlth Linyi 276000 Shandong Peoples R China NingboTech Univ Sch Comp & Data Engn Ningbo 315100 Peoples R China Imperial Coll London Dept Bioengn London SW7 2AZ England

With the growing popularity of fitness, the demand for real-time action recognition and feedback is increasing. Current research faces challenges in handling complex actions, real-time processing, and system integration. address these issues, we propose a novel fitness action recognition model that integrates ResNet, Transformer, and transfer learning techniques. Specifically, ResNet is used for image feature extraction, Transformer handles time-series data processing, and transfer learning accelerates the model's adaptation to new data. We evaluated our model on the NTU RGB+D action recognition dataset, achieving 48.5 ms latency, 29.1 fps throughput, and 93.7% accuracy, significantly outperforming other models. Our model achieved an accuracy improvement 5% over existing methods, demonstrating significant potential for real-time fitness monitoring. By incorporating IoT technology, our system enables real-time data processing and action recognition, making it ideal for smart fitness monitoring. Although the model has high complexity and memory usage, its efficiency and accuracy demonstrate its potential for widespread adoption. Future work will focus on optimizing the model structure and training methods to enhance applicability in resource-constrained environments, ensuring broader usability and efficiency in various real-world applications.

关键词： Action recognition image analysis ResNet Transformer Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Enhanced synthetic aperture radar image autofocus and classification using 2D SARNet framework

引用

JOURNAL OF APPLIED REMOTE SENSING 2024年第2期18卷

作者： Sakr, Mohamed Saleh, Ahmed AbdElkader, Fathy Amer, Ghada AboElenean, Mohamed Mil Tech Coll Elect Engn Cairo Egypt October 6 Univ Fac Informat & Comp Sci Giza Egypt MUST Univ Fac Engn Dept Elect Engn Giza Egypt

A synthetic aperture radar (SAR) system is a notable source of information, recognized for its capability to operate day and night and in all weather conditions, making it essential for various applications. SAR image formation is a pivotal step in radar imaging, essential for transforming complex raw radar data into interpretable and utilizable imagery. Nowadays, advancements in SAR sensor design, resulting in very wide swaths, generate a massive volume of data, necessitating extensive processing. Traditional methods of SAR image formation often involve resource-intensive and time-consuming postprocessing. There is a vital need to automate this process in near-real-time, enabling fast responses for various applications, including image classification and object detection. We present an SAR processing pipeline comprising a complex 2D autofocus SARNet, followed by a CNN-based classification model. The complex 2D autofocus SARNet is employed for image formation, utilizing an encoder-decoder architecture, such as U-Net and a modified version of ResU-Net. Meanwhile, the image classification task is accomplished using a CNN-based classification model. This framework allows us to obtain near real-time results, specifically for quick image viewing and scene classification. Several experiments were conducted using real-SAR raw data collected by the European remote sensing satellite to validate the proposed pipeline. The performance evaluation of the processing pipeline is conducted through visual assessment as well as quantitative assessment using standard metrics, such as the structural similarity index and the peak-signal-to-noise ratio. The experimental results demonstrate the processing pipeline's robustness, efficiency, reliability, and responsivity in providing an integrated neural network-based SAR processing pipeline.

关键词： synthetic aperture radar (SAR) deep learning CNN-based model ResU-Net SAR autofocus

来源：评论

学校读者我要写书评

暂无评论

A Regret Bound for the AdaMax Algorithm With image Segmentation Application

引用

MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2025年第9期48卷 10208-10214页

作者： Jirakipuwapat, Wachirapong King Mongkuts Univ Technol North Bangkok KMUTNB Fac Sci Energy & Environm Rayong Thailand

The AdaMax algorithm provides enhanced convergence properties for stochastic optimization problems. In this paper, we present a regret bound for the AdaMax algorithm, offering a tighter and more refined analysis compared to existing bounds. This theoretical advancement provides deeper insights into the optimization landscape of machine learning algorithms. Specifically, the You Only Look Once (YOLO) framework has become well-known as an extremely effective object segmentation tool, mostly because of its extraordinary accuracy in real-time processing, which makes it a preferred option for many computer vision applications. Finally, we used this algorithm for image segmentation.

关键词： AdaMax deep learning image segmentation regret bound YOLO

来源：评论

学校读者我要写书评

暂无评论

A deep-learning Empowered, real-time processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback

引用

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2025年 33卷 1220-1230页

作者： Xia, Yunjia Chen, Jianan Li, Jinchen Gong, Tingchen Vidal-Rosas, Ernesto E. Loureiro, Rui Cooper, Robert J. Zhao, Hubin Univ Coll London UCL Div Surg & Intervent Sci HUB Intelligent Neuroengn HUBIN CREATe London WC1E 6BT England Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England UCL Dept Med Phys & Biomed Engn DOT HUB London WC1E 6BT England

Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system's high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.

关键词： real-time systems US Department of Transportation Hemodynamics Functional near-infrared spectroscopy Optical imaging Three-dimensional displays Feedback amplifiers Jacobian matrices image reconstruction Electroencephalography Functional near-infrared spectroscopy (fNIRS) diffuse optical tomography (DOT) brain-computer interface (BCI) neurofeedback (NFB) motion artifacts deep learning real-time processing

来源：评论

学校读者我要写书评

暂无评论

Scientific preparation for CSST: classification of galaxy and nebula/star cluster based on deep learning

引用

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2024年第4期527卷 11935-11944页

作者： Zhang, Yuquan Cao, Zhong Wang, Feng Lam, Man, I Deng, Hui Mei, Ying Tan, Lei Guangzhou Univ Sch Elect & Commun Engn Guangzhou 510006 Peoples R China Guangzhou Univ Ctr Astrophys Guangzhou 510006 Peoples R China Natl Astron Data Ctr Great Bay Ctr Guangzhou 510006 Guangdong Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Chinese Acad Sci Natl Astron Observ Beijing 100101 Peoples R China

The Chinese Space Station Telescope (abbreviated as CSST) is a future advanced space telescope. real-time identification of galaxy and nebula/star cluster (abbreviated as NSC) images is of great value during CSST survey. While recent research on celestial object recognition has progressed, the rapid and efficient identification of high-resolution local celestial images remains challenging. In this study, we conducted galaxy and NSC image classification research using deep learning methods based on data from the Hubble Space Telescope. We built a local celestial image data set and designed a deep learning model named HR-CelestialNet for classifying images of the galaxy and NSC. HR-CelestialNet achieved an accuracy of 89.09 per cent on the testing set, outperforming models such as AlexNet, VGGNet, and ResNet, while demonstrating faster recognition speeds. Furthermore, we investigated the factors influencing CSST image quality and evaluated the generalization ability of HR-CelestialNet on the blurry image data set, demonstrating its robustness to low image quality. The proposed method can enable real-time identification of celestial images during CSST survey mission.

关键词： methods: data analysis techniques: image processing

来源：评论

学校读者我要写书评

暂无评论

real-time Moving Object Detection for 3-D LiDAR Using Occlusion Accumulation in Range image

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Kim, Junha Kim, Haram Oh, Changsuk Kim, Changhyeon Kim, H. Jin Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 08826 South Korea Seoul Natl Univ Automat & Syst Res Inst ASRI Seoul 08826 South Korea 42dot Seoul 13449 South Korea Seoul Natl Univ Artificial Intelligence Inst AIIS Seoul 08826 South Korea Samsung Res Seoul 06765 South Korea Seoul Natl Univ Dept Aerosp Engn Seoul 08826 South Korea

deep learning methods have been applied to detect moving objects from 3-D LiDAR data, but they require extra computing devices, such as graphics processing units (GPUs), and often struggle to operate at real-time LiDAR frame rates. In addition, retraining is necessary for new environments, which demands significant resources. Nonlearning methods that operate online without prior maps and rely solely on a central processing unit (CPU) often fail to achieve real-time performance. Furthermore, they are limited in applicability due to their dependence on specific pose estimation algorithms. To address these issues, we propose a novel occlusion accumulation framework in the range image domain for real-time 3-D LiDAR moving object detection on a CPU. By incorporating compensation strategies that account for LiDAR artifacts and measurement sparsity, the proposed method reduces false positives (FPs) and improves detection accuracy. Our approach is also flexible, integrating seamlessly with different pose estimation algorithms without performance degradation. Extensive experiments on KITTI, Apollo, and CARLA datasets demonstrate that the proposed method achieves competitive results compared to state-of-the-art (SOTA) learning-based methods on KITTI and outperforms them in cross-validation on Apollo and CARLA. Moreover, the proposed method operates four times faster than SOTA nonlearning-based methods while offering higher detection accuracy, making it highly suitable for real-world applications in autonomous driving and robotic navigation. We provide all the source code and datasets used in this article to the public at: https://***/JunhaAgu/Mapless_Moving.

关键词： 3-D LiDAR moving object detection occlusion accumulation occlusion accumulation range image range image range image

来源：评论

学校读者我要写书评

暂无评论

image processing framework for in-process shaft diameter measurement on legacy manual machines

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第9-10期135卷 4323-4338页

作者： Choudhari, Sahil J. Singh, Swarit Anand Kumar, Aitha Sudheer Desai, Kaushal A. Indian Inst Technol Jodhpur Dept Mech Engn Jodhpur 342030 Rajasthan India

In-process dimension measurement is critical to achieving higher productivity and realizing smart manufacturing goals during machining operations. Vision-based systems have significant potential to serve for in-process dimensions measurements, reduce human interventions, and achieve manufacturing-inspection integration. This paper presents early research on developing a vision-based system for in-process dimension measurement of machined cylindrical components utilizing image-processing techniques. The challenges with in-process dimension measurement are addressed by combining a deep learning-based object detection model, You Only Look Once version 2 (YOLOv2), and image processing algorithms for object localization, segmentation, and spatial pixel estimation. An automated image pixel calibration approach is incorporated to improve algorithm robustness. The image acquisition hardware and the real-time image processing framework are integrated to demonstrate the working of the proposed system by considering a case study of in-process stepped shaft diameter measurement. The system implementation on a manual lathe demonstrated robust utilities, eliminating the need for manual intermittent measurements, digitized in-process component dimensions, and improved machining productivity.

关键词： Dimension measurement In-process inspection Machine vision image processing Object segmentation deep learning

来源：评论

学校读者我要写书评

暂无评论

deep Convolution Neural Networks for image-Based Android Malware Classification

引用

Computers, Materials & Continua 2025年第3期82卷 4093-4116页

作者： Amel Ksibi Mohammed Zakariah Latifah Almuqren Ala Saleh Alluhaidan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadhP.O.Box 11442Saudi Arabia

The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of *** resolve this problem,efficient and flexible malware detection tools are *** work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data ***,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:***,***,***,***,*** network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained *** addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional *** success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.

关键词： Android malware detection deep convolutional neural network(DCNN) image processing CIC-AndMal2017 dataset exploratory data analysis VGG16 model

来源：评论

学校读者我要写书评

暂无评论

X-RCRNet: An explainable deep-learning network for COVID-19 detection using ECG beat signals

引用

BIOMEDICAL SIGNAL processing AND CONTROL 2024年 87卷

作者： Nkengue, Marc Junior Zeng, Xianyi Koehl, Ludovic Tao, Xuyuan Univ Lille Lab Genie & Mat Text GEMTEX ENSAIT F-59000 Lille France Univ Lille Ecole Cent Lille F-59000 Lille France

Wearable systems measuring human physiological indicators with integrated sensors and supervised learning -based medical image analysis (e.g. ECG, X-ray, CT or ultrasound images for lung or the chest) have been considered relevant tools for COVID-19 monitoring and diagnosis. However, these two technical roadmaps have their respective advantages and drawbacks. The current wearable systems enable to realize real-time monitoring of COVID-19 but are limited to its basic symptoms only, neither allowing to distinguish it from other diseases nor performing deep analysis. Current medical image analysis can provide accurate decision support for diagnosis but rarely deals with real-time data processing. In this context, we propose a new wearable system by combining the advantages of these two technical roadmaps. Considering that electrocardiogram (ECG) has been proved relevant to evolution of COVID-19 symptoms, the proposed wearable system will integrate an explainable deep Neural Network to realize online monitoring of COVID-19 gravity by using ECG beat signal. This paper will focus on the deep Neural Network model named X-RCRNet. The network is based on ResNet18 but with few en-hancements: 1) LSTM Layers for regenerating the backpropagation error and further extracting the involved time-varying features;2) LeakyReLU for increasing the performances of the model. With an accuracy of 96.48 % after experiments, our model has not only outperformed the existing methods in terms of accuracy and robustness, but also originally identify the ST interval of the ECG pattern, as the most prominent key features affected by the virus.

关键词： Explainable artificial intelligence Multilabel classification COVID-19 Signal processing

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：