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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

共 13662 条记录，以下是1291-1300 订阅

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Performance Evaluation of YOLOv4 for Instant Object Detection in UAVs

Performance Evaluation of YOLOv4 for Instant Object Detectio...

引用

IEEE Signal processing and Communications Applications (SIU)

作者： Gül Bahar Keskinoglu Nimet Selen Yesilkaya Irem Bayraktar Ercan Kose Aerospace Engineering Department Tarsus University Mersin Turkey

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Unmanned Aerial Vehicles (UAVs) have become integral in various research domains due to the advantages they provide. Current UAV systems rely on Global Navigation Satellite systems (GNSS) for flight control and sensors for obstacle detection, yet fully autonomous decision-making remains a challenge. This study evaluates the performance of YOLOv4, a Convolutional Neural Network (CNN) image recognition algorithm, for instantaneous object detection and classification in UAV-captured aerial images. The study demonstrates the applicability of YOLOv4 in real-time object detection and classification through UAV image feeds. The proposed approach advances the understanding of deploying CNNs in UAVs, offering a cost-effective solution for real-time object detection and classification, essential for autonomous UAV operations.

关键词： Performance evaluation Global navigation satellite system Signal processing algorithms Object detection Signal processing Autonomous aerial vehicles Real-time systems

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image Recognition System Based on Distributed Intelligence Technology to Optimize User Experience

Image Recognition System Based on Distributed Intelligence T...

引用

Network, Multimedia and Information Technology (NMITCON) International conference on

作者： Yihang Huang Visual Design Major Graduate School of Design Hanyang University Ansan Korea

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

With the continuous progress of computer technology, distributed intelligent systems become more and more popular. Based on this background, this paper discusses the theory of image recognition based on distributed intelligent network model and aims to implement the user location function through this theory. In this paper, an automatic detection algorithm based on distributed intelligence technology is proposed for implementing the function of user location. This algorithm is based on the theory of image recognition based on distributed intelligent network model and processes the image data through a specific algorithmic logic in order to locate the user’s position. The applicability of the algorithm in real projects is verified by writing experimental programmes. The Distributed Intelligence technique was compared with the other three algorithms and the results showed that the Distributed Intelligence technique performed best in terms of adaptability and stability. The comprehensive data exceeds 85.5%, indicating that the algorithm has high accuracy and efficiency in recognising and processing images, solves the problem of matching the target with the environmental scene, and provides an important reference and comparison basis for image acquisition and processing. The concluding observation of this paper is that the Distributed Intelligence Technology has the best comprehensive performance in different environments, not only superior to other algorithms in terms of adaptability and stability, but also its comprehensive data index exceeds 85.5%.

关键词： Intelligent networks image recognition Accuracy Web services Object oriented modeling Stability criteria Distributed databases Writing Mathematical models User experience

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Patch based image processing for complex environment characterization

Patch based image processing for complex environment charact...

引用

International image processing, Applications and systems conference (IPAS)

作者： Corentin Menier Cyril Meurie Timothée Guillemaille Yassine Ruichek Juliette Marais Univ Gustave Eiffel COSYS-LEOST Villeneuve d’Ascq France UTBM Belfort France

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Environment analysis is a critical part of autonomous vehicle for transport applications and for passenger safety. The solutions demonstrating the greatest robustness have been integrating multiple sensors used for redundancy and refinement purposes. Vision applications have proven to offer a high degree of flexibility and performance. One particular instance of this is higlighted in vehicle localisation, which predominantly relies on GNSS-based systems for positioning calculation using propagation time measurements. However, this signal may be degraded through the environment around the vehicle, worst case being urban canyons leading to Non Line Of Sight(NLOS) scenarios or multipaths issues due to reflecting obstacles. Previous work have shown vision-based algorithms can be used to mitigate these effects. One widely studied approach relies on the segmentation of an acquired wide-angle image installed on the roof of the vehicle and oriented toward the *** the sky processing module is binary, the pipeline lack any way to express its uncertainty when applying weighting policies to the detected satellite state, which can be detrimental to the resulting positioning. In this paper, we propose a novel way of analysing wide-angle camera images, also known as fisheye images, dividing the image into patches to output the corresponding situation of each region of interest. Additionally we propose a new class to the previous sky versus non-sky segmentation, designated as mixed class and designed to serve as a fuzzy answer by the deep learning model to improve confidence to other scenarios as well as allow for new analysis policies of satellites signals. The data-driven algorithm is designed and tested on a publicly available dataset, composed of a large number of finely labelled images provided by ISAE-SUPAERO reaching a 94% accuracy.

关键词： Training Global navigation satellite system Visualization Satellites Three-dimensional displays Accuracy Uncertainty Vegetation mapping Cameras Trajectory

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Arbitrary Array Distributed ISAR Three-Dimensional Imaging of Ship Targets

Arbitrary Array Distributed ISAR Three-Dimensional Imaging o...

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International conference on Vision, image and Signal processing (ICVISP)

作者： Junqiu Zhang Yong Wang Research Institute of Electronic Engineering Technology Harbin Institute of Technology Harbin China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

In the distributed radar systems, the high array alignment accuracy is often required to enable the coherent processing, however achieving this is challenging to achieve in practical applications. This study proposes a three-dimensional (3-D) radar imaging method for ships based on an arbitrary array configuration within a distributed interferometric inverse synthetic aperture radar (ISAR) system. And each base station consists of an interferometric with three orthogonal channels. Firstly, the separated base stations observe the ship target from arbitrary angles to acquire the echoes of the target from different observation angles. Each station then reconstructs a three-dimensional image of the target through the optimal time selection method and the interferometric technique. These 3-D images, presented as point clouds, contain complementary information due to different observation angles. Finally, a 3-D image fusion technique is applied to obtain the final three-dimensional image. The effectiveness and robustness of the proposed method have been validated through simulation experiments.

关键词： Point cloud compression Base stations Three-dimensional displays Imaging Signal processing algorithms Radar imaging Robustness Marine vehicles Inverse synthetic aperture radar image reconstruction

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ICMSSP 2022 - 2022 7th International conference on Multimedia systems and Signal processing

ICMSSP 2022 - 2022 7th International Conference on Multimedi...

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7th International conference on Multimedia systems and Signal processing, ICMSSP 2022

ISBN: (纸本)9781450396424

The proceedings contain 15 papers. The topics discussed include: application of color filtering and image filtering algorithms in circuit diagnosis of smart meter box;research on brick carving based on 3D digital modeling technology — take the example of Linxia brick sculpture art ‘River and Mountain’;improving identity-relevant deepfake video detection in real-world with adversarial data augmentation;land evolution analysis of Waisanding sandbar using Google Earth;research on course clustering based on semantic;application study of mobile technology assisted translation teaching mode in applied universities;on the influence and application of artificial intelligence on China’s commercial sports industry — empirical analysis based on artificial intelligence coupling model;efficiency enhancement and mode change of intelligent media communication under the interaction of artificial intelligence technology;and on the mechanism of artificial intelligence affecting sports tourism industry.

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Pesticide Recommender System for Detecting the Paddy Crop Diseases through SVM

Pesticide Recommender System for Detecting the Paddy Crop Di...

引用

IEEE International conference on Intelligent Techniques in Control, Optimization and Signal processing (INCOS)

作者： Josephine Selle Jeyanathan Manoranjan Kumar P.Ravi Kumar Reddy G.Chakradhar Reddy D.Karthik Reddy Y.Shaik Rabbani Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Virudhunagar Nanochipskills (Venkylabs) Bengaluru Karnataka India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Rice cultivation is pivotal in India, facing various color-related issues during different growth stages. Identifying these conditions manually can be challenging, especially for less experienced farmers. Recent advancements in Deep Learning (DL) demonstrate the effectiveness of automated image recognition systems. These systems utilize Convolutional Neural Networks (CNN), Transfer Learning (TL), and Support Vector Machine (SVM) models for similar agricultural problems. Due to the unavailability of readily accessible rice disease image datasets, a custom dataset was created. Consequently, a deep learning model was devised using transfer learning. The proposed CNN architecture, based on MobileNet, was trained and tested using datasets from paddy fields and the internet.

关键词： Deep learning Support vector machines Plant diseases Transfer learning Signal processing algorithms Signal processing Sugar industry

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Smart Lane Detection Using Open CV and image processing 3

Smart Lane Detection Using Open CV and Image Processing

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3rd International conference on Power, Energy, Control and Transmission systems, ICPECTS 2022

作者： Suresh, K. Gandhi, A Sanjeevi Soumya, M. Dakshina, R. Sri Sai Ram Engineering College Department of EEE Chennai India

ISBN: (纸本)9781665462754

Lane detection has become popular in real time vehicular lane detection. The Lane detection plays a major role in Driver Assistance System. Our paper aims to improve results of lane detection by using various algorithms which reduce noise visibility, picture quality, etc. This will be helpful in improving the performance of existing algorithm for lane detection. In case if any noise persists in the form of fog or any type of obstacle such as dust, shadow, puddles, oil stains and tyre skid marks in the road image, the existing methods fails to give efficient results. So, the overall goal of this paper is to evaluate the gaps in the existing methodology and to give suitable solution for the same. In this paper we have developed a Entropy based fusion model (EBFM), simple method and to improve the results of lane detection and object detection based on Arduino. In Heuristic method, clustering methodology is used to group the detected points and a best fit line in the mean square sense to detect the lanes. © 2022 IEEE.

关键词： Advanced driver assistance systems

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The Software Verification Tool Examines the Research and Application of Intelligent Robots

The Software Verification Tool Examines the Research and App...

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Information Technology, Electronics and Intelligent Communication systems (ICITEICS), IEEE International conference on

作者： Jianyang Zhang Mancheng Yi Feng Luo Yanxia Wei Tao Chen Wenrui Wang CSG Guangdong Guangzhou Power Supply Bureau Guangdong province China

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

In view of the limitations of traditional image recognition algorithms in the research and application of inspection intelligent robots, a research and application scheme of intelligent robots based on software verification tools is proposed. Firstly, the influencing factors is accurately located through the accounting and positioning theory, and the indicators is reasonably divided to reduce interference, and the research and application scheme of intelligent robots is constructed by using software verification tools. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional image recognition algorithm in terms of research and application accuracy of intelligent robots, and processing time of influencing factors, which has obvious advantages. The research and application of intelligent robots plays an extremely important role in the intelligent era, which can accurately predict and optimize the growth characteristics and product generation in the intelligent era. However, traditional image recognition algorithms have certain limitations in solving the research and simulation problems of robots, especially when dealing with complex problems. In this paper, this paper proposes a research and application scheme of intelligent robot based on software verification tools to better solve this problem. The scheme accurately locates the influencing factors through the accounting positioning theory, so as to determine the division of indicators, and uses software verification tools to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on software verification tools can better solve the limitations of traditional image recognition algorithms and improve the simulation accuracy and efficiency in the research and application of i

关键词： Accuracy image recognition Software algorithms Key performance indicator Inspection Prediction algorithms Software

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A 640x512 30μm Pixel Pitch 1.8mK-NETD 90.1dB-SNR Digital Read-out Integrated Circuit with Fully On-chip image Algorithm Pixel-Level Calibration

A 640x512 30μm Pixel Pitch 1.8mK-NETD 90.1dB-SNR Digital Re...

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17th IEEE Asian Solid-State Circuits conference (A-SSCC) - Integrated Circuits and systems for the Connection of Intelligent Things

作者： Zeng, Yan Yang, Shiheng Liu, Yueduo Li, Zehao Huang, Wengang Huang, Xiaozong Zhou, Xiong Liu, Jiaxin Li, Qiang Univ Elect Sci & Technol China Chengdu Peoples R China China Elect Technol Grp Corp Res Inst 24 Chongqing Peoples R China

ISBN: (纸本)9781665443500

Infrared focal plane arrays (IRFPAs) have been widely used in industrial, scientifical and medical imaging applications [1]–[5]. Due to the limitation of the manufacturing process, material quality and other factors, IRFPAs inevitably suffer from the current non-uniformity under the same light intensity; and the bad pixel with extreme photoelectric response which significantly degrade the quality of the image. In addition to the bad pixel compensation and non-uniformity calibration, some other processing algorithms are often implemented for IRFPAs to improve the image quality, such as spatial filtering, background subtraction, etc. Conventionally the image processing algorithms are implemented by caching one or more frames of images via on-chip memory or FPGA first, and then the algorithmic processing is performed on the cashed image data. It takes up large amounts of resource space to store the data, and the need of multiple image algorithms not only increases the system complexity but also prolongs the processing time significantly [1].

关键词： image quality Manufacturing processes Filtering image processing conferences System-on-chip Calibration

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Enhancing Palm Recognition Accuracy Through image processing Deep Learning Techniques

Enhancing Palm Recognition Accuracy Through Image Processing...

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Data Science and Network Security (ICDSNS), International conference on

作者： J. Sheela Mercy S. Silvia Priscila Department of Computer Applications Bharath Institute of Higher Education and Research Chennai India Department of Computer Science Bharath Institute of Higher Education and Research Chennai India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Palm recognition systems play an important role in biometric authentication; however, existing systems frequently have low accuracy and resiliency due to problems such as changing lighting conditions, occlusions, and hand orientations. The paper describes a novel way to improve palm recognition accuracy by combining modern image processing and deep learning approaches. Unlike traditional systems that utilize handcrafted features and shallow learning algorithms, the proposed system uses convolutional neural networks (CNNs) for feature extraction and classification, as well as advanced image preprocessing techniques. The technology preprocesses palm images to increase quality and consistency, thereby reducing the impact of environmental factors. Cross-validation trials yielded consistent results, with an average accuracy of 95%, precision of 92%, recall of 96%, and Fl-score of 94%. Furthermore, computational efficiency comparisons reveal that the proposed system beats existing ones in terms of training time, inference time, and model size, indicating that it is a promising option for accurate and efficient palm recognition in a variety of real-world applications.

关键词： Training Deep learning Accuracy image recognition Computational modeling Lighting Biometric authentication

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