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Application of intelligent real-time image processing in fitness motion detection under internet of things

JOURNAL OF SUPERCOMPUTING 2022年第6期78卷 7788-7804页

作者： Cai, Hang Nanjing Normal Univ Sch Sports Sci & Phys Educ Nanjing 210000 Jiangsu Peoples R China

The purpose is to study the applicability of digital and intelligent real-time image processing (IP) in fitness motion detection under the environment of the Internet of Things (IoT). Given the absence of real-time training standards and possible workout injury problems during fitness activities, an intelligent fitness real-time IP system based on deep learning (DL) is implemented. Specifically, the keyframes of the real-time images are collected from the fitness monitoring video, and the DL algorithm is introduced to analyze the fitness motions. Afterward, the performance of the proposed system is evaluated through simulation. Subsequently, the Noise Reduction (NR) performance of the proposed algorithm is evaluated from the Peak Signal-to-Noise Ratio (PSNR), which remains above 20 dB for seriously noisy images (with a noise density reaching up to 90%). By comparison, the PSNR of the Standard Median Filter (SMF) and Ranked-order Based Adaptive Median Filter (RAMF) algorithms are not higher than 10 dB. Meanwhile, the proposed algorithm outperforms other DL algorithms by over 2.24% with a detection accuracy of 97.80%;the proposed system can adaptively detect the fitness motion, with a transmission delay no larger than 1 s given a maximum of 750 keyframes. Therefore, the proposed DL-based intelligent fitness real-time IP algorithm has strong robustness, high detection accuracy, and excellent real-time image diagnosis and processing effect, thus providing an experimental reference for sports digitalization and intellectualization.

关键词： deep learning real-time image processing Internet of Things Fitness motion Artificial intelligence

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Condition Monitoring of Railway Pantograph Using R-CNN and image processing

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第3期18卷 2407-2416页

作者： Na, Kyung-Min Lee, Kiwon Kim, Hyungchul Railway Components Evaluat Ctr Korea Testing Lab Seoul South Korea Korea Railrd Res Inst Smart Elect & Signaling Div Uiwang South Korea

A railway pantograph supplies a vehicle with the electric power from the OCL (Overhead Contact Line). The contact strip, which directly contacts the OCL, is vulnerable to wear and should be periodically replaced to prevent accidents from power outages. To that end, the pantograph is regularly checked visually during routine maintenance, along with the panhead which includes the horn and the contact strip. However, real-time monitoring is still difficult, and a reliable method needs to be developed. The existing methods use image processing to detect the actual condition of the contact strip. In this paper, we suggest a method for detecting the wear size of the contact strip using image processing and deep learning. In addition, we demonstrate how to assess the condition of the panhead and the horn in the contact. The monitoring equipment is built on the test bed and is automatically configured to acquire images of the moving pantograph. The tilt of the panhead is estimated from image recognition and the condition of the horn is assessed with deep learning. In the future, this method can be used not only for efficient pantograph maintenance but also for determining the performance according to the contact condition.

关键词： Electric railway Pantograph Contact strip Maintenance

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real-time pose estimation and motion tracking for motion performance using deep learning models

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JOURNAL OF INTELLIGENT SYSTEMS 2024年第1期33卷

作者： Liu, Long Dai, Yuxin Liu, Zhihao Chongqing Presch Educ Coll Sch Hlth Care Chongqing 404047 Peoples R China Chongqing Presch Educ Coll Sch Phys Educ Chongqing 404047 Peoples R China

With the refinement and scientificization of sports training, the demand for sports performance analysis in the field of sports has gradually become prominent. In response to the problem of low accuracy and poor real-time performance in human pose estimation during sports, this article focused on volleyball sports and used a combination model of OpenPose and deepSORT to perform real-time pose estimation and tracking on volleyball videos. First, the OpenPose algorithm was adopted to estimate the posture of the human body region, accurately estimating the coordinates of key points, and assisting the model in understanding the posture. Then, the deepSORT model target tracking algorithm was utilized to track the detected human pose information in real-time, ensuring consistency of identification and continuity of position between different frames. Finally, using unmanned aerial vehicles as carriers, the YOLOv4 object detection model was used to perform real-time human pose detection on standardized images. The experimental results on the Volleyball Activity Dataset showed that the OpenPose model had a pose estimation accuracy of 98.23%, which was 6.17% higher than the PoseNet model. The overall processing speed reached 16.7 frames/s. It has good pose recognition accuracy and real-time performance and can adapt to various volleyball match scenes.

关键词： motion performance pose estimation motion tracking deep learning models real-time performance

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Optimizing Federated learning With deep Reinforcement learning for Digital Twin Empowered Industrial IoT

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第2期19卷 1884-1893页

作者： Yang, Wei Xiang, Wei Yang, Yuan Cheng, Peng Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China La Trobe Univ Sch Comp Engn & Math Sci Melbourne Vic 3086 Australia James Cook Univ Coll Sci & Engn Cairns Qld 4870 Australia

The accelerated development of the Industrial Internet of Things (IIoT) is catalyzing the digitalization of industrial production to achieve Industry 4.0. In this article, we propose a novel digital twin (DT) empowered IIoT (DTEI) architecture, in which DTs capture the properties of industrial devices for real-time processing and intelligent decision making. To alleviate data transmission burden and privacy leakage, we aim to optimize federated learning (FL) to construct the DTEI model. Specifically, to cope with the heterogeneity of IIoT devices, we develop the DTEI-assisted deep reinforcement learning method for the selection process of IIoT devices in FL, especially for selecting IIoT devices with high utility values. Furthermore, we propose an asynchronous FL scheme to address the discrete effects caused by heterogeneous IIoT devices. Experimental results show that our proposed scheme features faster convergence and higher training accuracy compared to the benchmark.

关键词： deep reinforcement learning (DRL) digital twin (DT) federated learning (FL) Industrial Internet of Things (IIoT) learning efficiency real time

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SLTM Network: Efficient Application of Lightweight image Segmentation Technology in Detecting Drivable Areas for Unmanned Line-Marking Machines

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IEEE ACCESS 2024年 12卷 169001-169012页

作者： Wang, Chao Chen, Xiangkai Wang, Bingtao Zhang, Liang Liu, Bing Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China Shandong Univ Off Acad Affairs Weihai 264209 Peoples R China

image segmentation plays a crucial role in the roadwork operations of autonomous line-painting machines. However, the limited resources of mobile platforms in intelligent line-painting applications pose a dual challenge of ensuring both accuracy and real-time performance in road segmentation. To address this issue, this study introduces a lightweight yet efficient image segmentation model, termed the SLTM Network. Central to this network is the lightweight SLTM module, which significantly reduces the model's parameter count and lowers the computational overhead of the decoder. To enhance the interplay of information at different spatial resolutions, the network incorporates an SE attention-enhanced upsampling module (SAUM) and employs a Spatial Attention Sequence (SAS) unit to improve global environment perception at a low computational cost. Comprehensive experimental evaluations on the Cityscapes dataset demonstrate that the SLTM Network excels in balancing speed and accuracy, achieving an mIoU of 70.5% with only 4.07M parameters and an impressive inference speed of 267.1 FPS. On the embedded device Jetson Xavier NX, it achieves an inference speed of 34.2 FPS. Compared to existing lightweight image segmentation models, the SLTM Network exhibits significant advantages in both processing speed and accuracy, making it particularly suitable for real-time autonomous line-painting machine applications.

关键词： Roads image segmentation Feature extraction Computer architecture Computational efficiency Synthetic aperture sonar Semantics Lightweight image segmentation unmanned line-marking machines deep learning in autonomous driving embedded systems optimization SLTM network real-time road segmentation

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Estimating Water Levels through Smartphone-imaged Gauges: A Comparative Analysis of ANN, DL, and CNN Models

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WATER RESOURCES MANAGEMENT 2025年第4期39卷 1639-1654页

作者： Santos, Celso Augusto Guimaraes Ghorbani, Mohammad Ali Abdi, Erfan Patel, Utkarsh Sadeddin, Siria Univ Fed Paraiba Dept Civil & Environm Engn BR-58051900 Joao Pessoa PB Brazil Univ Tabriz Dept Water Engn Tabriz Iran Indian Inst Technol IIT Bhilai Main Campus Durg 491001 Chhattisgarh India Univ Nacl Colombia Math Dept Bogota Colombia

Understanding and monitoring water levels are essential for various applications, including environmental protection, public safety, and resource management. Water level estimation, a critical aspect of hydrological monitoring, is often constrained by challenges such as resource scarcity, high costs, and time-intensive processes. This research addresses these limitations by developing a machine learning-based system for automatic and real-time water level control. Specifically, it investigates the effectiveness of a non-contact, image-based water level measurement approach, leveraging recent advancements in mobile imaging technology. images were captured using a standard smartphone equipped with an RGB camera for water level analysis. Through precise image alignment processing under both clear and turbid conditions, the water's edge on a gauge was accurately detected. The study centers on the development and comparison of three computational models: Artificial Neural Networks (ANN), deep learning (DL), and Convolutional Neural Networks (CNN). These models were trained to estimate water levels from processed image data. Results demonstrated varying levels of accuracy across models, with the CNN model outperforming others, achieving the lowest error rate of 24.36 mm and the highest correlation of 0.986. In contrast, the ANN model yielded the highest error rate at 30.76 mm and the lowest correlation of 0.968, highlighting the relative effectiveness of CNN in this application. Given the high accuracy (92.6%) of the image processing method and CNN model in detecting water surface edges and determining water levels, this system has substantial potential to enhance water resource management and control efficiency.

关键词： Computational Hydrology Convolutional Neural Networks Hydrological Monitoring image Analysis Non-contact Measurement

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real-time deep learning-Based Object Recognition in Augmented reality

Real-Time Deep Learning-Based Object Recognition in Augmente...

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Conference on real-time processing of image, Depth, and Video Information

作者： Egipko, V Zhdanova, M. Gapon, N. Voronin, V. Semenishchev, E. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Rostov Na Donu Russia

ISBN: (纸本)9781510673199;9781510673182

Augmented reality is a visualization technology that displays information by adding virtual images to the real world. Effective implementation of augmented reality requires recognition of the current scene. Identifying objects in real-time video on computationally limited hardware requires significant effort. One way to solve this problem is to create a hybrid system that, based on machine learning and computer vision technology, processes and analyzes visual data to identify and classify real-world objects. The proposed architecture is based on a combination of the Vuforia augmented system, which provides good performance by balancing prediction accuracy and efficiency. First, the Vuforia neural network architecture allows convenient interaction with AR in Unity and provides initial conditions for detecting 3D objects. The augmented reality construction algorithm is based on the ARCore framework and the OpenGL interface for embedded systems. The system integrates recognition data with an AR platform to display corresponding 3D models, allowing users to interact with them through the functionality of the AR application. This method also involves the development of an enhanced user interface for AR, making the augmented environment more accessible for navigation and control. Experimental research has shown that the proposed method significantly improves the accuracy of object recognition and the ease of working with 3D models in AR.

关键词： deep learning augmented reality computer vision object recognition robotic systems real-time processing

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Artifact reduction in photoacoustic images by generating virtual dense array sensor from hemispheric sparse array sensor using deep learning

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JOURNAL OF MEDICAL ULTRASONICS 2024年第2期51卷 169-183页

作者： Yamakawa, Makoto Shiina, Tsuyoshi Shibaura Inst Technol SIT Res Labs 3-7-5 ToyosuKoto Ku Tokyo 1358548 Japan

PurposeVascular distribution is important information for diagnosing diseases and supporting surgery. Photoacoustic imaging is a technology that can image blood vessels noninvasively and with high resolution. In photoacoustic imaging, a hemispherical array sensor is especially suitable for measuring blood vessels running in various directions. However, as a hemispherical array sensor, a sparse array sensor is often used due to technical and cost issues, which causes artifacts in photoacoustic images. Therefore, in this study, we reduce these artifacts using deep learning technology to generate signals of virtual dense array *** 2D virtual array sensor signals using a 3D convolutional neural network (CNN) requires huge computational costs and is impractical. Therefore, we installed virtual sensors between the real sensors along the spiral pattern in three different directions and used a 2D CNN to generate signals of the virtual sensors in each direction. Then we reconstructed a photoacoustic image using the signals from both the real sensors and the virtual *** evaluated the proposed method using simulation data and human palm measurement data. We found that these artifacts were significantly reduced in the images reconstructed using the proposed method, while the artifacts were strong in the images obtained only from the real sensor *** the proposed method, we were able to significantly reduce artifacts, and as a result, it became possible to recognize deep blood vessels. In addition, the processing time of the proposed method was sufficiently applicable to clinical measurement.

关键词： Photoacoustic imaging Sparse array sensor Hemispherical array sensor deep learning Artifact reduction

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Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorithms

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DISCOVER FOOD 2025年第1期5卷 1-23页

作者： Ajith, S. Vijayakumar, S. Elakkiya, N. Int Rice Res Inst New Delhi 110012 India ICAR Indian Inst Rice Res Hyderabad 500030 India ICAR Cent Inst Brackishwater Aquaculture Chennai 600028 Tamil Nadu India

The growing demand for food grains amidst resource constraints necessitates advancements in crop management. Artificial intelligence, particularly machine learning and deep learning, is revolutionizing agricultural practices by enabling data-driven, precise, and sustainable solutions. This review synthesizes advancements in artificial intelligence applications across key domains, including crop yield prediction, precision irrigation, soil fertility mapping, insect pest and disease forecasting, and foodgrain quality assessment. Artificial intelligence algorithms efficiently process vast datasets from unmanned aerial vehicles, ground vehicles, and satellites, enabling precise and timely interventions. Artificial intelligence-driven tools automate pest detection and classification, optimize irrigation with minimal human input, generate high-resolution soil fertility maps, and enhance foodgrain quality assessment through rapid defect and contaminant detection. Artificial intelligence-powered precision irrigation integrates real-time soil moisture data and weather predictions for optimized water usage. Similarly, artificial intelligence-driven soil fertility mapping not only enables high-resolution assessments but also facilitates real-time monitoring of nutrient dynamics, supporting sustainable land management. In pest and disease detection, artificial intelligence systems combining image processing and real-time analytics demonstrate promise for early intervention. Artificial intelligence integration into foodgrain quality assessment leverages hyperspectral imaging and predictive models to enhance grading, adulteration detection, and contaminant screening, contributing to food safety and market competitiveness. Furthermore, advancements in transfer learning and data augmentation have improved artificial intelligence adoption in regions with limited datasets. While artificial intelligence technologies promise to boost agricultural productivity and sustainability, their

关键词： Artificial intelligence Nutrient management Soil moisture Crop protection Yield forecasting

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Fast partition algorithm in depth map intra coding unit based on multi-deep convolution neural network

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JOURNAL OF real-time image processing 2024年第1期21卷 23-23页

作者： Omran, Nacir Maraoui, Amna Werda, Imen Hamdi, Belgacem Fac Sci Monastir Elect & Microelect Lab Environm St Monastir 5019 Tunisia Univ Sfax Elect & Informat Technol Lab Sfax Tunisia

The three-dimension high-efficiency video coding standard (3D-HEVC) finalized comes with a significant increase in complexity caused by the integration of depth map coding technology. This complexity is primarily triggered by the quad-tree partition of the Intra Coding Units (CU) in the depth map. A new technique utilizing deep learning is proposed, in this paper, to tackle the issue of excessive complexity aiming to predict efficiently the CU partition structure. The proposed method involves building a dataset of CU partition structure information for a depth map, creating a Multi-deep Convolutional Neural Network (MD-CNN) model using this dataset, and then incorporating the model into the 3D-HEVC test platform. This approach reduces the 3D-HEVC video encoder complexity by 48.29% without affecting robustness, compression efficiency and video quality.

关键词： deep CNN 3D-HEVC Complexity Reduction Quad-tree CU partition

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