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检索条件"任意字段=Automation and Inspection Applications of Image Processing Techniques 1977"

共 106 条记录，以下是21-30 订阅

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An overview of visual servoing for robotic manipulators in digital agriculture 43

An overview of visual servoing for robotic manipulators in d...

引用

43. Jahrestagung der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft - Resiliente Agri-Food-Systeme: Herausforderungen und Losungsansatze, GIL 2023 - 43rd Annual Conference of the Society for Informatics in Agriculture, Forestry, and Food Industry - Resilient Agri-Food Systems: Challenges and Solutions, GIL 2023

作者： Shamshiri, Redmond R. Dworak, Volker Zeini, Mostafa Shokrian Navas, Eduardo Käthner, Jana Höfner, Nora Weltzien, Cornelia Technische Universität Berlin Fachgebiet Agromechatronik Straße des 17. Juni 135 Berlin10623 Germany Centre for Automation and Robotics UPM-CSIC Carretera CAMPO- REAL Km 0.2 Arganda del Rey Madrid28500 Spain Leibniz Institute for Agricultural Engineering and Bioeconomy Max-Eyth-Allee 100Bornim Potsdam14469 Germany

ISBN: (纸本)9783885797241

Innovations in terms of robotic manipulator control in digital agriculture have advanced considerably in the last decade with the aim of reducing costs and increasing efficiencies. The availability of compact imaging sensors such as digital cameras that can perceive depth information besides the flexibility of open-source image processing software that can be trained for different applications has played significant roles in accelerating this sector. Preliminary studies have shown that the majority of available robot manipulators in agriculture are using image-Based Visual Servo (IBVS) control to reach a target position. The presented study provides an overview of different redundant manipulators that are controlled by means of visual servoing for automating various field tasks in digital agriculture including (i) pruning, thinning, and trimming, (ii) harvesting, and (iii) inspection and target spraying. The reviewed works suggest that developing optimal tree shapes and planting techniques is necessary to improve the performance of visual servo control and automate farming operations with robots. In addition, selection of the right imaging sensors, graphics processing units, and training of the computer vision algorithms with more fruits and plants datasets have been highlighted as the three main elements for improving the functionality of IBVS in manipulator control for agricultural applications. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Agricultural robots

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Application of Unmanned Aerial System (UAS) in Highway Construction Progress Monitoring automation

Application of Unmanned Aerial System (UAS) in Highway Const...

引用

Construction Research Congress (CRC) on Project Management and Delivery, Contracts, and Design and Materials

作者： Samsami, Reihaneh Mukherjee, Amlan Brooks, Colin N. Michigan Tech Univ Dept Civil Environm & Geospatial Engn Houghton MI 49931 USA Michigan Tech Univ Michigan Tech Res Inst MTRI Ann Arbor MI USA

ISBN: (数字)9780784483961

ISBN: (纸本)9780784483961

The objective of this research is to propose a highway construction progress monitoring system using Unmanned Aerial System (UAS) technologies, computer vision techniques, and data analytic tools to automatically: (1) estimate the construction operation production, and (2) monitor the progress relative to an as-planned 3D model. The motivation of this research is to assist project managers in automatically and efficiently monitoring construction project progress during the construction phase. The proposed progress monitoring process consists of three steps of (1) collection of orthophotographs of ongoing construction operations using UAS, (2) use of image processing to support automated feature extraction, and (3) development of as-planned vs. as-built progress estimates based on a comparison of as-planned model and as-built reports. The method is implemented and tested for pavement removal and concrete pavement construction operations for the Michigan Department of Transportation (MDOT) I-496 corridor reconstruction project. The main contribution of this paper is to further automation approaches into highway construction inspection workflows.

关键词： automation

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Survey on image-Oriented Transmission Line inspection via UAV

Survey on Image-Oriented Transmission Line Inspection via UA...

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Computer Science and automation Technology (CSAT), International Conference on

作者： Ran Quan Feng Sun Xiaoping Hu Miao Yu Jinxu Wang Zixu Li State Grid Jilin Electric Power Co. Ltd. Changchun China Shenyang Jiuzhou Feixiang Tech. Co. Ltd Shenyang China CCST Jilin University Changchun China

Unmanned aerial vehicle (UAV) is exploited gi-gantically to substitute the human beings in the wild natural working environments to face the challenges of severe climate situations and low job efficiency. This paper surveys the reported applications of UAV in long-distance transmission line inspection via the research and techniques in image processing. It discusses the major pros and cons of the state of the art methods and discloses the future working directions to benefit the research domain.

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Viewpoint Planning for Fuel Leak Detection in Rail-Mounted inspection Robot

Viewpoint Planning for Fuel Leak Detection in Rail-Mounted I...

引用

Machine Learning and Computer Application (ICMLCA), International Conference on

作者： Sheng Zhu Shuyue Zhang Kanjian Zhang Haikun Wei School of Automation Southeast University Nanjing China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

This paper proposes a fuel leak detection method designed for unmanned Antarctic energy platforms, applied to a rail-mounted inspection robot. By introducing a viewpoint planning strategy and utilizing vector geometry to calculate perspective features, the robot can automatically optimize its camera angles to capture images from the best viewpoints. Additionally, this paper employs multi-scale Retinex and high-pass filtering techniques to enhance image processing and achieve uniform lighting, significantly improving the accuracy of fuel leak detection. Experimental results demonstrate that this method exhibits excellent detection performance in simulated environments, validating its feasibility for practical applications.

关键词： Geometry Accuracy Filtering inspection Vectors Antarctica Planning Fuels Leak detection Robots

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Histogram-Based image Enhancement and Analysis for Steel Surface and Defects images

Histogram-Based Image Enhancement and Analysis for Steel Sur...

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International Conference on Machine Vision and Augmented Intelligence, MAI 2021

作者： Kumar, Ranjeet Soni, Anil Kumar Soni, Aradhana Gupta, Saurav School of Electronics Engineering Vellore Institute of Technology Chennai Tamilnadu600127 India Guru Ghasidas Vishwavidyalaya Bilaspur Chattisgarh495009 India Guru Ghasidas Vishwavidyalaya Bilaspur Chattisgarh495009 India

ISBN: (纸本)9789811650772

In the industrial and domestic applications, iron and steel manufacturers are a move towards automation in several ways like production, inspection and delivery of products. In this chain process, quality inspection of steel/iron plats or rods is very necessary to deliver the quality product and its life. At present, visual inspection using image processing technology is utilized for surface quality or defect visibility. Here, the visual inspection system gives a breakthrough low-cost solution as compared to other methods like non-destructive testing (NDT) testing. However, image quality is also a matter of concern to inspect the surface quality of the material. In this paper, an analysis of image visual quality enhancement techniques based on histogram processing is presented for the steel surface and defect images such as rolled-in scale (RS), patches (Pa), crazing (Cr), pitted surface (PS), inclusion (In) and scratches (Sc) on the steel surface. Further, different enhancement techniques are validated with an approximation quality metrices and visual analysis process for original and enhanced images. This analysis illustrates these techniques are suitable for image quality enhancement of grayscale steel surface images as tested on 12 images of 6 different defect subjects from Northeastern University (NEU) surface defect database of 1800 images. In the automation process, image quality help to extract accurate information from images in different applications like segmentation and features extraction for classification or detection of defects of healthy surface condition. These advantages are clearly illustrated in the results and its analysis that claim the suitability of histogram-based image enhancement technique for steel images. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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Autonomous Path Follow UAV to Assist Onshore Pipe inspection Tasks 7

Autonomous Path Follow UAV to Assist Onshore Pipe Inspection...

引用

7th International Conference on Robotics and automation Engineering (ICRAE)

作者： Sousa, Lucas C. da Silva, Yago M. R. de Castro, Gabriel G. R. Souza, Caio L. Berger, Guido Lima, Jose Brandao, Diego Dias, Joao T. Pinto, Milena F. CEFET RJ Fed Ctr Technol Educ Celso Suckow Fonseca Rio De Janeiro RJ Brazil Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal Inst Politecn Braganca Lab Sustentabilidade & Tecnol Regioes Montanha Braganca Portugal INESC Technol & Sci Porto Portugal

ISBN: (纸本)9781665489188

Unmanned Aerial Vehicles (UAVs) are being deployed in different applications due to their reduced time execution to perform tasks, more extensive coverage area, and more risk minimization to humans. In the Oil & Gas industry, its use for inspection activities is even more attractive due to the large structures in these facilities. Therefore, this research proposes deploying an autonomous UAV system to inspect unburied pipelines of onshore O&G facilities. The proposed UAV guiding system is based on image processing techniques Canny edge detection and Hough Transform to detect the line and on a path follower algorithm to generate the trajectory. The proposed strategy was developed in Robot Operating System (ROS) and tested in a simulated environment considering the practical oper-ational. The same controller was tested on a physical UAV to validate the results obtained in previous simulations. The results demonstrated the effectiveness and feasibility of deploying the proposed strategy for this specific task and the cost reduction potential for real-life operations, as well as reduced potential risks to the physical integrity of the workers.

关键词： pipeline inspection unmanned aerial vehicle computer vision oil and gas industry

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PCB Component inspection System Using Raspberry Pi

PCB Component Inspection System Using Raspberry Pi

引用

Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC), IEEE

作者： M Deepa Ms Kaniskaa K Hariprasath S Eashwar M Subbu Department of Electronics and Communication Engineering PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (数字)9798350384659

ISBN: (纸本)9798350384666

High-performance vision inspection systems that can analyze enormous volumes of unstructured data are in high demand in the field of industrial automation. This project outlines a novel strategy to meet this requirement by emphasizing the application side of image collecting and processing rather than the underlying *** is built to function flawlessly in industrial environments while collecting photos from various sources. These photos go through a number of sophisticated image processing techniques because they are frequently filled with complex and unstructured data. The system’s strengths cover a wide range of industrial fields, such as dimensions analysis, defect identification, and object recognition. It offers quick data processing and high levels of accuracy through careful algorithm design and optimization, making it a vital asset in improving industrial *** industrial applications, this system handles unorganised data exceptionally well. Industries are able to improve product quality, lower faults, and streamline operations because to its powerful picture analysis capabilities. We highlight the system’s potential to lead to substantial developments and enhancements across many industrial sectors by concentrating on its actual applications. The proposed system uses Raspberry Pi 2 board as the processor along with the image processing algorithms and R-CNN algorithm using OpenCV library in Python for comparison of the images. Thus, the model is trained effectively to meet the requirements and inspect PCB components successfully.

关键词： Training Industries Process control inspection Streaming media Product design Libraries

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Enhancing Agricultural Productivity: A Sensor Fusion, Spectroscopic Near Infrared, and AI-Based Framework for Monitoring Fruit Ripeness

Enhancing Agricultural Productivity: A Sensor Fusion, Spectr...

引用

International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Hira Ambreen Muhammad Imran Tang Diyin Chongsheng Zhang Sir Syed CASE Institute of Technology Islamabad Pakistan School of Automation Science and Electrical Engineering Beihang University Aeronautics and Astronautics China Department of EE Military College of Signals National University of Sciences & Technology (NUST) Henan Univerity Kaifeng China Department of Detection and Automation Engineering School of Automation Science and Electrical Engineering Beihang University Aeronautics and Astronautics China School of Computer & Information Engineering Henan University Kaifeng China

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

Fruit ripeness evaluation is essential for harvest optimization and post-harvest loss prevention. Traditional visual inspection is inefficient. This study employs sensor fusion and AI algorithms to evaluate fruit maturity in real-time using gas sensors (i.e., MQ-3, MQ-6, MQ-8, MQ-135) and Near-Infrared spectral sensors (i.e., GY-AS7263). Primarily, data pro-cessing-sensor fusion, data preparation, and AI model application-is crucial to the proposed framework. Subsequently, sensor fusion approaches improve data accuracy by combining signals from various sensors. Secondly, data pretreatment techniques including noise reduction, normalization, and feature extraction prepare data for analysis. Thirdly, we use powerful machine learning algorithms such as Random Forest to judge maturity more accurately and reliably than conventional techniques. Lastly, integrating NIR spectroscopy with gas sensor data resolves cost and model transferability issues, enhancing accuracy and acces-sibility for agricultural use. Static image processing techniques provide challenges for real-time applications, including expensive computer costs and lower reliability. The proposed framework based on multi-sensor fusion system enhances agricultural applications' versatility, scalability, and reactivity, surpassing existing methodologies.

关键词： Productivity Accuracy Machine learning algorithms Noise reduction Sensor fusion Feature extraction Real-time systems Reliability Monitoring Gas detectors

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods

Detection of Detonators in Baggage Using Deep CNN Architectu...

引用

International Conference on image and Signal processing and their applications (ISPA)

作者： Oulhissane Lynda Merah Mostefa Simona Moldovanu Luminita Moraru Laboratory of Signals and Systems LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Mostaganem Algeria MSlab Department of Computer Science and Information Technology Modelling & Simulation Laboratory Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania MSlab Department of Chemistry Physics & Environment Modelling & Simulation Laboratory Faculty of Sciences and Environment Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL

关键词： Wavelet transforms Radiography Visualization Gray-scale inspection Feature extraction Libraries

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A Deep Learning Based Strategies for Scene-Text VQA System used in Industrial applications: A Critical Analysis

A Deep Learning Based Strategies for Scene-Text VQA System u...

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Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS), International Conference on

作者： Mayank Agrawal Anand Singh Jalal Himanshu Sharma Department of Computer Engineering and Applications GLA University Mathura India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

Scene-Text Visual Question Answering (Scene-Text VQA) is an emerging research area that combines computer vision, natural language processing, and scene understanding. The goal of Scene-Text VQA is to develop models and algorithms that can comprehend scene text and accurately answer questions based on textual and visual information present in images. Scene-Text VQA models used in different areas such as quality control and inspection, equipment maintenance, safety compliance, supply chain visibility, industrial automation, industry 4.0, assistance for the visually impaired, document analysis, autonomous vehicles, e-commerce, smart cities, cultural heritage preservation, surveillance, and security. Scene-Text VQA models face unique challenges, including accurate text detection and recognition, understanding contextual cues, handling language complexities, fusing text and image features effectively, limited training data, biases in datasets, generalization to unseen text and scenes, computational complexity, and appropriate evaluation metrics. In this paper we will explain different methods and algorithms for Scene-Text VQA models that can comprehend scene text and accurately answer questions based on textual and visual information present in images. The authors will distinguish all the techniques of Scene-Text VQA for effectively process and comprehend the textual information present in images and show how can one technique is conflicting then the other as well.

关键词： Measurement Visualization image recognition Text analysis Text recognition Computational modeling Face recognition

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