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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321-330 订阅

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Design and Development of vision Based Mobile Robot for Warehouse Application 2

Design and Development of Vision Based Mobile Robot for Ware...

引用

2nd IEEE North Karnataka Subsection Flagship international conference, NKCon 2023

作者： Namith, N. Balappa, B.U. Ravichandran, Shama Msruas Dept. of Mechanical and Manufacturing Engineering Bangalore India Msruas Dept. of Electrical and Electronics Engineering Bangalore India

ISBN: (纸本)9798350314045

This paper presents a vision based mobile robot for transporting materials from warehouse to workstation. The robot operates in two modes namely human following mode and line following mode. The robot utilizes a tag-following algorithm to accurately track humans and makes use of line following for navigation. It minimizes manual labor and boosts efficiency. It revolutionizes warehouse operations, enhancing navigation, and operational adaptability which represents a significant step forward in warehouse automation. Three types of tests, namely control test, maneuver test and load bearing test, are performed to test the effectiveness of the robot. Test results show that the mobile robot successfully carried loads up to 6 kgs and undergoes accurate maneuverability and control without colliding with obstacles. © 2023 IEEE.

关键词： Mobile robots

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Cultivating Precision: Unmanned Aerial Vehicle Retrieval Enhancement through Computer vision in Ground Robot Recovery Missions 15

Cultivating Precision: Unmanned Aerial Vehicle Retrieval Enh...

引用

15th IEEE international conference on Humanoid, Nanotechnology, Information Technology, Communication and control, Environment, and Management, HNICEM 2023

作者： Clar, Steve E. Banglos, Charles Alver G. Pao, Jeanette C. Aleluya, Earl Ryan M. Alagon, Francis Jann A. Salaan, Carl John O. Guirnaldo, Sherwin A. Graduate School of Engineering Mindanao State University - Iligan Institute of Technology Iligan City Philippines

ISBN: (纸本)9798350381177

Robot exploration is a rapidly advancing domain within robotics, with applications ranging from disaster relief to space exploration. Unmanned aerial vehicles (UAVs), commonly called drones, have become essential tools in these scenarios due to their agility and extensive workspace coverage. However, their limited battery life remains a challenge. To address this, collaborations between UAVs and ground robots have gained traction. This article focuses on the retrieval operation pro-cess in such collaborative systems. Specifically, it addresses the localization of the retrieval component attached to the drone. Prior study used ArUco markers and computer vision to detect the drone's position. However, due to the environmental factors, such as sunlight, makes it hard to operate outside and rely on this method. Thus, this study aims to address this with three key objectives: designing and fabricating a prototype retrieval component, training object detection model using YOLOv4-tiny, and evaluating the performance of the retrieval mechanism process. The results showcase the system's adeptness in detecting the retrieval component using the trained model with an accuracy of 89%, achieving a mere 1.41% average error in area estimation and a commendable 1.28% average error in estimating the retrieval component's distance to the ground robot's center. These findings underscore the system's precision, ith potential implications for diverse real-world scenarios. © 2023 IEEE.

关键词： Antennas

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Deep Learning-Based vision System for Water Stress Classification of Lettuce in Pot Cultivation 15

Deep Learning-Based Vision System for Water Stress Classific...

引用

15th IEEE international conference on Humanoid, Nanotechnology, Information Technology, Communication and control, Environment, and Management, HNICEM 2023

作者： Flores, Earl John C. Gonzaga, Jeremias A. Augusto, Gerardo L. Chua, Joseph Aldrin T. Gan Lim, Laurence A. Gokongwei College of Engineering De la Salle University Department of Mechanical Engineering Manila Philippines

ISBN: (纸本)9798350381177

The increase in global population has resulted in a corresponding rise in the need for food and water, highlighting the significance of optimizing the utilization of scarce agricultural resources. Water scarcity is a major issue that affects crop growth, yield, and quality. Traditional plant water stress detection methods are time-consuming, expensive, and impractical for large-scale applications. To address this, researchers are developing non-invasive vision-based techniques that detect water stress in plants accurately utilizing vision systems and deep learning. Applying vision systems and deep learning in agriculture can help improve traditional agricultural practices and advance technology in the agriculture sector. This work aimed to develop a MobileNetV2-SVM model to classify and detect the degree of water stress in lettuce. According to the findings, the model achieved an accuracy of 87.50%. Implementing the system to detect mild water stress in green ice lettuce resulted in a notable improvement in crop output and a decrease in water usage. This implies that implementing a vision-based deep learning model to detect and classify water stress in green ice lettuce has the potential to boost water productivity, achieving more yield with less water. © 2023 IEEE.

关键词： Lettuce

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Third international conference on Computer Technology, Information engineering, and Electron Materials, CTIEEM 2023

Third International Conference on Computer Technology, Infor...

引用

3rd international conference on Computer Technology, Information engineering, and Electron Materials, CTIEEM 2023

ISBN: (纸本)9781510672925

The proceedings contain 26 papers. The topics discussed include: a Stuxnet-style malware propagation modeling study based on Tesla autonomous vehicles;radar emitter recognition method based on kernel extreme learning machine optimized by sparrow search algorithm;analyzing fusion plasma diagnostic images based on computer vision and deep learning;research on trust mechanism in wireless sensor networks;YOLOv7-based attention and improved feature fusion small object detection algorithm;research on control of single-phase photovoltaic energy storage grid-connected inverter;a civil unmanned aircraft intelligent control system based on multi-source sensor;and construction of quantitative prediction model of metal mineral resources based on big data mining.

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vision Transformers for Breast Cancer Classification from Thermal Images

Vision Transformers for Breast Cancer Classification from Th...

引用

international conference on robotics, control, and Computer vision, ICRCCV 2022

作者： Garia, Lalit S. Hariharan, M. 246174 India ECE Department BTKIT Dwarahat Uttarakhand Almora263653 India

ISBN: (纸本)9789819902354

Breast cancer is the leading reason for cancer-related fatality among women. Timely diagnosis can improve the survival rate of women suffering from breast cancer. Thermography is a non-contact and non-invasive imaging technique extensively used in the field of breast cancer detection. Thermography is studied as an efficient screening tool capable to detect breast cancer earlier than the traditional mammography methods. The computer-aided system can escalate the rate of diagnosis, treatment, and survival of the patient. In recent times, vision Transformer (ViT), which is developed on self-attention within image patches, exhibited a great future to be a replacement for CNNs. In this analysis, ViT is utilized to classify breast thermal images for the first time using two different ViT models. The results are presented as classification accuracy and Area under the Curve (AUC) metrics. The results indicate that the ViT models show resembling efficiency with or even better than the CNNs for breast thermal image classification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Development of a Heavy Truck Reversing Safety System based on Pedestrian Detection and Tracking using Binocular vision Stitching

Development of a Heavy Truck Reversing Safety System based o...

引用

2023 international conference on Image, Signal Processing, and Pattern Recognition, ISPP 2023

作者： Yu, Jiaxi Wu, Xiaoping Li, Dongyang Lu, Linhai Southwest Jiaotong University School of Mechanical Engineering Chengdu610031 China China Railway Wuhan Electrification BureauGroup Shanghai Electric Co. Ltd Shanghai201799 China Jinan Rail Transit Group Co. Ltd Jinan250014 China

ISBN: (纸本)9781510666351

The large blind region behind the huge body of a truck is the primary reason for reversing collisions. However, there are few truck reversing auxiliary approaches to avoid reversing accidents. In this paper, we present a novel heavy truck reversing safety approach based on pedestrian detection and tracking using binocular vision stitching. The proposed system contains three primary steps: binocular vision stitching, pedestrian detection and tracking, and heavy truck reversing speed control method. First of all, a binocular camera system based on vision stitching is used to perceive the reversing environment of the heavy truck. Secondly, the framework of YOLOv3 and discriminative correlation filter based tracking method is used to detect and track the pedestrian in real time. The pedestrian feature is extracted with improved MobileNetV2. Finally, a heavy truck reversing speed control method which can automatically control the reversing speed to avoid collisions improves the safety of truck reversing. This system has been tested on a van truck. Experiments demonstrate the viability of the proposed heavy truck reversing safety system. © 2023 SPIE.

关键词： Binocular vision

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Modification of the Canny Edge Detector for Recognition of Image Markers of Architectural Plans 7

Modification of the Canny Edge Detector for Recognition of I...

引用

7th international conference on Information, control, and Communication Technologies, ICCT 2023

作者： Aksionova, Natallia A. Demidenko, Oleg M. Varuyeu, Andrei V. Sych, Denis S. F. Skorina Gomel State University Dept. Asip Gomel Belarus

ISBN: (纸本)9798350340945

The article describes an algorithm used for recognition of image markers of design and estimate documents of the developer's architectural plans using computer vision technology. The main goal of the study is to propose a pre-processing method capable of improving and revealing the features of the given images and solving the problem of noise reduction. The article describes the analysis of the Canny operator and considers its modification, where the Chambollele algorithm-based Rudin-Osher-Fatemi model is used for image pre-processing. The method described is effective in detection of edges on images of architectural plans. An approbation of the developed software modules and a comparative analysis of the results of the algorithms were carried out. © 2023 IEEE.

关键词： Computer vision

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Classification of Weld Defects Based on Computer vision System Data and Deep Learning

Classification of Weld Defects Based on Computer Vision Syst...

引用

2023 international conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2023

作者： Cherkasov, Nikita Ivanov, Mikhail Ulanov, Aleksey Institute of Engineering and Technology South Ural State University National Research University Chelyabinsk Russia

ISBN: (纸本)9781665475952

Visual and dimensional inspection is widely used in the search for external weld defects that locate on the surface of welded joints. Such inspections are carried out by trained specialists whereas computer vision-based quality control systems require the significant investments. In addition, the data about the object of scanning that can be obtained during the operation of computer vision systems irretrievably disappear, or remain in the memory of the system without their processing and any analysis to find probable patterns of welding defects. This study utilized the capabilities of the iRvision system, which is built into a Fanuc welding robot, to obtain the data about the weld joint after robotic welding. Such data could be accumulated with the help of the 3D Laser Scanning subsystem's laser sensor. Pre-processing of the collected data was carried out. And the artificial neural networks, using deep learning, were subsequently developed for the classification of the data of welds into defective and non-defective. The results of the most effective of the developed neural network models are an accuracy of 0.92, a precision of 0.90, and a recall of 0.81 on the test sample. The presented results allow us to automate the quality control and visual inspection process of welded joints based on the data from the iRvision system. The research has shown that the perspective direction of the project is to improve the model to search for the small size defects of © 2023 IEEE.

关键词： Neural networks

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vision-Based Categorical Object Pose Estimation and Manipulation 16th

Vision-Based Categorical Object Pose Estimation and Manipul...

引用

16th international conference on Intelligent robotics and Applications, ICIRA 2023

作者： Meng, Qiwei Liao, Jianfeng Jun, Shao Xu, Nuo Xu, Zeming Sun, Yinan Sun, Yao Zhu, Shiqiang Gu, Jason Song, Wei Center for Intelligent Robotics Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China School of Mechanical Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964826

Object manipulation and environment interaction are of great significance for intelligent robots, especially service robots working under unstructured household and office scenarios. This paper proposes a novel approach for categorical unseen object grasping and manipulation. Different from recently popular end-to-end reinforcement learning methods, we develop models for geometric primitive abstraction of target objects, and accordingly estimate their pose as well as generate task-orientated grasp points. Such design emphasizes visual perception in guiding robotic manipulation, thereby enhancing model interpretability and reliability during implementation. In addition, we also conduct object grasping experiments both under simulation and real-world settings, which further verify the effectiveness and superiority of our method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intelligent robots

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Proceedings of the 2023 24th international Carpathian control conference, ICCC 2023

Proceedings of the 2023 24th International Carpathian Contro...

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24th international Carpathian control conference, ICCC 2023

ISBN: (纸本)9798350310221

The proceedings contain 93 papers. The topics discussed include: case study: optimization of pi controller gains with anti-windup applied on a level system;industrial application of hydrophone for condition monitoring of water pump;sliding mode control of a 3-DOF helicopter with state dependent regional pole placement;accuracy testing of a mobile robot position identifying in 3D space;autonomous robot control using hardware accelerated implementation of fuzzy interpolation;assessment of the possibilities of using statistical process control tools in metallurgical production;from toddlers to experts - the education of robotics;the use of anti-windup techniques in didactic level systems: an interesting application;and acoustic analysis of a performance hall using virtual techniques.

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