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检索条件"任意字段=2023 International Conference on Robotics, Control and Vision Engineering, RCVE 2023"

共 1303 条记录，以下是221-230 订阅

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Research on Distance Perception engineering Quantity Accounting Technology of Transmission Line Grounding Device Based on Binocular Stereo vision AnyNet Network 3

Research on Distance Perception Engineering Quantity Account...

引用

3rd international conference on Computer Science, Electronic Information engineering and Intelligent control Technology, CEI 2023

作者： Zhang, Liping Li, Li Wang, Nianxiao Csg Guangdong Power Grid Corporation Guangzhou China

ISBN: (纸本)9798350372052

This paper studies the distance perception engineering quantity accounting technology of transmission line grounding device based on binocular stereo vision AnyNet network. This paper introduces a binocular stereo matching algorithm using deep learning to estimate the depth of the scene. By combining lightweight binocular stereo matching technology and deep neural network, high-precision grounding grid depth length recognition is realized. This method can quickly generate high-precision disparity map through multi-stage prediction and disparity image element correction, improve the accuracy and efficiency of engineering quantity accounting, reduce human error, and ensure the objectivity and reliability of accounting results. © 2023 IEEE.

关键词： Electric lines

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Machine vision Techniques for Cervical Intraepithelial Neoplasia Detection

Machine Vision Techniques for Cervical Intraepithelial Neopl...

引用

2023 international conference on Power, Instrumentation, control and Computing, PICC 2023

作者： Kapruwan, Akanksha Goyal, Himanshu Rai Sharma, Sachin Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350334463

Cervical intraepithelial neoplasia (CIN) is a precancerous condition of the cervix that, if left untreated, can develop into cervical cancer. Preventing cervical cancer requires the early detection and management of CIN. Machine vision techniques have the potential to greatly enhance CIN-C diagnosis and treatment. These methods can be used to examine digital images of cervical cells and determine whether any aberrant cells are present based on characteristics like size, shape, colour, and texture. Additionally, patterns in cervical cytology samples that are suggestive of CIN can be recognized by machine learning algorithms. The accuracy and specificity of CIN-C diagnosis can also be increased by combining machine vision with other technologies like optical coherence tomography and spectroscopy. Additionally, the development of portable and low-cost cervical cancer screening systems can increase access to healthcare in resource-limited settings. This paper reviews the current state of the art in machine vision techniques for CIN-C detection, highlighting recent developments and future perspectives in this field. © 2023 IEEE.

关键词： Computer vision

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Research and Analysis of a Computer vision Based Algorithm for Identifying Ash Deposits in Air Condensing Condensers 6

Research and Analysis of a Computer Vision Based Algorithm f...

引用

6th international conference on Intelligent robotics and control engineering, IRCE 2023

作者： Wang, Ming Hao Zhao, Bo Wang, Gong Wang, Xue Mei Yu, Jian Yu Chen, Nan Northeast Electric Power University Jilin China Jilin Electric Power Co. Ltd Changchun China

ISBN: (纸本)9798350326338

As one of the important equipment of fossil-fuel power station, air cooler is widely used in the three north regions of China, where coal is rich and water is scarce. Its heat exchange efficiency has a considerable impact on unit efficiency. Therefore, conducting intelligent identification and analysis of ash accumulation in air coolers and effectively cleaning them is of great significance for improving the power generation efficiency of power plants. Considering the color and texture changes caused by the degree of dust accumulation in the visible light image of the surface of the air cooler. A method of identifying and analyzing ash deposition in air cooler based on convolutional neural network transfer learning is proposed. We split the training set, test set, and validation set using 6:2:2. Using a convolutional neural network algorithm model to identify air cooler images with varying degrees of fouling, and mining and analyzing the corresponding relationship between air cooler fouling images and fin cleaning. The results show that for the existing 5 levels of air coolers with ash accumulation, the model with higher recognition rate is Vgg19, with recognition accuracy of 0.98 and recall rate of 0.98, respectively, while the model with lower recognition accuracy is AlexNet, with an accuracy rate of 0.78. The proposed method for analyzing the ash accumulation status of air coolers can directly, real-Time, and quantitatively analyze the on-site ash accumulation of air coolers, and provide reference and new considerations for achieving intelligent cleaning and operation and maintenance on site. © 2023 IEEE.

关键词： Textures

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Gamification of Physical Therapy Exercises using Commercial Entertainment Content: A Safety and Feasibility Study

Gamification of Physical Therapy Exercises using Commercial ...

引用

international conference on Rehabilitation robotics (ICORR)

作者： Amos, Giulia Rode, David Maffiuletti, Nicola A. Bizzini, Mario Mauch, Marlene Easthope, Chris Awai Haufe, Florian Xiloyannis, Michele Wolf, Peter Riener, Robert Swiss Fed Inst Technol Inst Robot & Intelligent Syst IRIS Sensory Motor Syst SMS Lab Zurich Switzerland Human Performance Lab Schulthess Clin Zurich Switzerland Rennbahnklin Muttenz Switzerland Univ Hosp Basel Dept Orthopaed & Traumatol Basel Switzerland Cereneo Fdn Ctr Interdisciplinary Res Vitznau Switzerland Lake Lucerne Inst Vitznau Switzerland Univ Zurich Balgrist Univ Hosp Fac Med Spinal Cord Injury Ctr Zurich Switzerland

ISBN: (纸本)9798350342758

Independent physiotherapy at home is a crucial element of rehabilitative care for a wide range of conditions as it constitutes a large portion of the overall therapy dose. However, up to 80% of individuals who are prescribed at-home physiotherapy do not consistently adhere to their treatment schedule, resulting in poor treatment outcomes. This is likely due to a lack of motivation and progress tracking in the current standard of care. We have developed a novel software prototype that allows users to control commercial entertainment content, such as video games or interactive music videos, with their movements during physiotherapy. By connecting therapy to proven entertainment content, we aim to improve on the current motivational deficits. This study investigated the safety and feasibility of this concept in a controlled environment over four physical therapy sessions with seven patients suffering from musculoskeletal and neurological conditions. As a secondary outcome, patients were asked about their enjoyment, perceived competence and effort using the Intrinsic Motivation Inventory (IMI) questionnaire. All participants were able to interact with the presented entertainment content and completed the study with no adverse events. Despite the diversity in pathology, age and training scenarios, the entertainment content maintained the patients' enjoyment with a high average rate of 6/7 on the IMI scale. Interacting with commercial entertainment content by doing physical therapy exercises was feasible, safe, and well-received over the six-week study period.

关键词： Physiotherapy Telerehabilitation Games Pose Estimation Computer vision Motivation

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Hopping Motion on Heavy-Legged Bipedal Robot Based on SLIP Model and Whole Body control

Hopping Motion on Heavy-Legged Bipedal Robot Based on SLIP M...

引用

2023 international conference on Frontiers of robotics and Software engineering, FRSE 2023

作者： Chen, Xueying Yang, Yong Cai, Wenhan Huang, Songrui Li, Xiu Zhao, Mingguo Tsinghua University Shenzhen International Graduate School Shenzhen China Tsinghua University Department of Automation Beijing China

ISBN: (纸本)9798350301113

Hopping is a challenging action of bipedal robots and is critical in robotic motions such as obstacle crossing. However, the inherent dynamics of the robot are so complex that only Superior-performance planning and control algorithms can achieve perfect hopping. Unfortunately, nonlinear model predictive control (NMPC) isn't easy to apply to a physical robot because it is computationally expensive. In this paper, we propose a planning method based on the spring loaded inverted pendulum (SLIP) model to generate the center of mass (CoM) trajectory for periodic hopping, and combine it with quadratic programming (QP)-based whole-body control (WBC) to resolve the control problem in real time. Since WBC can deal with the full dynamics of the robot and various constraints, SLIP model only needs to plan the robot CoM trajectory and the plantar force to provide a reference for the continuous hopping of the bipedal robot, thus greatly reducing the computational cost of the full dynamics model planning. The approach's validity was verified in simulation on a heavy-legged bipedal robot. Furthermore, our method is more adaptive than traditional control methods for heavy-legged biped robots. © 2023 IEEE.

关键词： Motion planning

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Design of Human Posture Recognition System Integrating Computer vision and Deep Learning Algorithm

Design of Human Posture Recognition System Integrating Compu...

引用

2023 international conference on Power, Electrical engineering, Electronics and control, PEEEC 2023

作者： Chu, Jinquan Wang, Haoyi Dong, Anxing Chen, Yuming Qufu Normal University Shandong Rizhao276800 China

ISBN: (纸本)9798350329124

In this article, a human posture recognition model combining computer vision and deep learning (DL) algorithm is proposed. Under the condition of keeping the data structure and sample labels unchanged, the model randomly cuts the video frame images by using the horizontal translation matrix, which effectively expands the data set samples, thus reducing the risk of over-fitting of the model to some extent. The experimental results show that the improved human target detection algorithm in this article can effectively improve the detection performance of the algorithm, and the computational complexity of the human posture recognition algorithm is small, which increases the correlation between adjacent video frames in the recognition process, and improves the accuracy of posture recognition while ensuring real-time. The realization of this algorithm is of great significance for building a simplified human posture recognition system with high response and accuracy. © 2023 IEEE.

关键词： Object detection

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Real-Time Upper Body Motion Tracking Using Computer vision for Improved Human-Robot Interaction and Teleoperation 9

Real-Time Upper Body Motion Tracking Using Computer Vision f...

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9th international Multidisciplinary Moratuwa engineering Research conference, MERCon 2023

作者： Nandasena, N.A.S.N. Vimukthi, W.A.A. Herath, H.M.K.K.M.B. Wijesinghe, R. Yasakethu, S.L.P. Sri Lanka Technological Campus Faculty of Engineering Padukka Sri Lanka

ISBN: (纸本)9798350345216

Upper body motion tracking mapping is crucial for robot control because it gives the machine a better understanding of how a human operator moves, allowing it to react instinctively and naturally. Most current research has focused on using wearable sensors and remote controls to enhance communication between robots and humans. However, this research aims to address the issue by embracing a non-wearable sensor-based strategy to promote more natural and spontaneous interactions between humans and robots. Moreover, A 3-DoF manipulator was also designed and developed utilizing robotics technologies. The vision system captured a human operator's upper body movements in realtime video footage. Computer vision approaches were used to extract positional and orientation information from the upper body in this setting. The system combines the MediaPipe pose model with kinematics theories to estimate the hands' position and movement in real-time. According to the experiment results, the system's overall accuracy is 94.1(\pm 1.2)\%, and the motion tracking system's accuracy is 96.5(\pm 2.0)\%. © 2023 IEEE.

关键词： Human robot interaction

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vision-Based Autonomous Navigation for Unmanned Surface Vessel in Extreme Marine Conditions

Vision-Based Autonomous Navigation for Unmanned Surface Vess...

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IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Ahmed, Muhayyuddin Bakht, Ahsan Baidar Hassan, Taimur Akram, Waseem Humais, Ahmed Seneviratne, Lakmal He, Shaoming Lin, Defu Hussain, Irfan Khalifa Univ Ctr Autonomous Robot Syst Abu Dhabi U Arab Emirates Abu Dhabi Univ Dept Elect Comp & Biomed Engn Abu Dhabi U Arab Emirates Beijing Inst Technol Beijing Peoples R China

ISBN: (纸本)9781665491907

Visual perception is an important component for autonomous navigation of unmanned surface vessels (USV), particularly for the tasks related to autonomous inspection and tracking. These tasks involve vision-based navigation techniques to identify the target for navigation. Reduced visibility under extreme weather conditions in marine environments makes it difficult for vision-based approaches to work properly. To overcome these issues, this paper presents an autonomous vision-based navigation framework for tracking target objects in extreme marine conditions. The proposed framework consists of an integrated perception pipeline that uses a generative adversarial network (GAN) to remove noise and highlight the object features before passing them to the object detector (i.e., YOLOv5). The detected visual features are then used by the USV to track the target. The proposed framework has been thoroughly tested in simulation under extremely reduced visibility due to sandstorms and fog. The results are compared with state-of-the-art de-hazing methods across the benchmarked MBZIRC simulation dataset, on which the proposed scheme has outperformed the existing methods across various metrics.

关键词： Navigation Marine robotics Visual control

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The Development of an Application to Analyse GigE vision Packets 24

The Development of an Application to Analyse GigE Vision Pac...

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24th international Carpathian control conference, ICCC 2023

作者： Forgacs, Zsofia Trohak, Attila University of Miskolc Institute of Automation and Infocommunication Miskolc Hungary

ISBN: (纸本)9798350310221

The focus of a previous research at the University of Miskolc was the determination of the speed of high-speed rotating parts. A non-contact measurement procedure using a line scan camera was developed. During the examination of the camera using the Gigabit Ethernet (GigE) vision standard interface, it was suggested that image data processing can be improved by pre-processing the Ethernet packets. Verification of this proposition requires the examination of GigE vision packets, which is the purpose of the application presented in this article. © 2023 IEEE.

关键词： Ethernet

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Low-Power VLSI Design for Image Analysis in Embedded vision Systems

Low-Power VLSI Design for Image Analysis in Embedded Vision ...

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2023 international conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manikandababu, C.S. Jagadeeswari, M. Irfan, H. Mohammed Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798350313987

An innovative low-power Very Large Scale Integration (VLSI) architecture for real-time image identification in embedded vision systems is presented in this research study. The proposed design combines the cutting-edge hardware optimizations, algorithmic improvements, and power management practices in response to the rising demand for energy-efficient solutions across several applications, including robotics, surveillance, and autonomous cars. It starts with an effective sensor interface that uses power-saving analog-to-digital converters and smart clock gating techniques to reduce power usage while data is being acquired. Processing units specially created for high-performance operation with less power consumption include hardware accelerators for convolutional neural networks, specialized feature extraction units, and dedicated image identification accelerators. An adequately designed memory hierarchy improves energy efficiency, low-power interconnects, clock gating, and voltage scaling. The proposed architecture is completed with features for data parallelism, power management, and algorithmic improvements. The system's power utilization, real-time performance, and image identification precision have all undergone extensive testing using benchmark datasets and real-world scenarios. The proposed architecture is a solution for embedded vision systems because it strikes the perfect mix between real-time image identification and power economy. © 2023 IEEE.

关键词： Convolutional Neural Network Embedded vision System Low-Power Very Large Scale Integration Power Management Real-time Image Detection

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