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

共 16443 条记录，以下是1061-1070 订阅

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FWAVs Object Detection Based on YOLOv8 In Adverse Weather

FWAVs Object Detection Based on YOLOv8 In Adverse Weather

引用

image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Junying Li Chenguang Zhai xiaojun Yang Fu Qiang School of Aeronautics Northwestern Polytechnical University Xi’an China National Key Laboratory of Aircraft Configuration Design

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In recent years, Flapping-wing Micro Aerial Vehicles (FMAVs) have gained increasing attention due to their biomimetic appearance and excellent maneuverability. Equipping them with object detection systems to make them more intelligent is becoming a major trend. This paper proposes an aerial photography small object detection algorithm under adverse weather conditions based on YOLOv8. Using a custom dataset, the concluding mAP50 (mean Average Precision at Intersection over Union threshold of 0.5) reached 0.761, surpassing other datasets. The mAP50 improved from 0.627 to 0.708 compared to model validation on the VisDrone2019 dataset, surpassing other mainstream object detection models.

关键词： Computer vision Machine learning algorithms Snow image processing Biomimetics Object detection Machine learning Market research Satellite images Meteorology

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An Event-Driven Approach to the Recognition Problem in Video Surveillance System Development 32

An Event-Driven Approach to the Recognition Problem in Video...

引用

32nd conference of Open-Innovations-Association-FRUCT

作者： Bazhenov, Nikita Rybin, Egor Korzun, Dmitry Petrozavodsk State Univ PetrSU Petrozavodsk Russia

ISBN: (数字)9789526924489

ISBN: (纸本)9789526924489

Many video surveillance systems (VSS) have been already developed for various application domains. Such systems are based on well-elaborated recognition algorithms of Artificial Intelligence (AI) and implemented as Ambient Intelligence (AmI) services in Internet of Things (IoT) environments. In particular, algorithms support such smart VSS functions of video data processing as human detection, human identification, object location within an image, human activity recognition. Many software tools have been developed to implement various recognition algorithms for VSS development. In this paper, we consider the following VSS development problems: a) a generic model of events in video data for a given problem domain, b) a hardware-software architecture for data processing with existing recognition algorithms, and c) a model to construct a required smart VSS function using existing software tools. We introduce our event-oriented approach to solve the above VSS development problems. The approach is experienced in several use cases. Our experimental study shows the applicability of the proposed approach in terms of the accuracy and performance.

关键词： Technological innovation Face recognition Software algorithms Computer architecture Data processing Video surveillance Data models

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Scanning Electron Microscope image Data Acquisition and Transmission System

Scanning Electron Microscope Image Data Acquisition and Tran...

引用

Intelligent Control, Measurement and Signal processing and Intelligent Oil Field (ICMSP), International conference on

作者： Yang Zhang Zuye Wang Yong Zhao School of Instrument Science and Opto-Electronics Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Scanning electron microscopy (SEM) has emerged as a pivotal tool in scientific research and engineering, offering unparalleled insights into the micro- and nano-scale world. However, traditional SEM systems often entail bulky setups and complex data acquisition processes, limiting their accessibility and efficiency in various applications. This paper designs a desktop SEM image data acquisition and transmission system based on FPGA. The system is divided into two parts: signal modulation and AD sampling circuit and image data processing and transmission circuit. By designing a circuit to process SE/BSE signals, signal modulation and analog-to-digital conversion are achieved. The sampling data is processed using the internal resources of the FPGA and transmitted to the PC through the USB3.0 high-speed interface, and finally displayed as a grayscale image. The system realizes real-time collection, processing and transmission of SEM signals, providing convenience and efficiency for scientific research and engineering applications. Experimental verification shows that the system performs well and has certain application potential.

关键词： Scanning electron microscopy Machine learning algorithms Microscopy Data acquisition Modulation Gray-scale Data processing Universal Serial Bus Real-time systems Field programmable gate arrays

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Convolutional Neural Network Based Defect Detection in Small Diameter Pipe Welds

Convolutional Neural Network Based Defect Detection in Small...

引用

6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Yu Guo Weixin Gao Zihan Wang School of Electronic Engineering Xi'an Shiyou University Xi'an China School of Information and Control Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Aiming at the current situation of low accuracy of X-ray weld image detection rate of small diameter pipe, the defect recognition algorithm of small diameter pipe weld image based on image segmentation is proposed by analysing the image and combining with convolutional neural network. Firstly, the ROI region is obtained by two-step image segmentation, then the SDR image is obtained by grey density clustering, and finally the SDR image is classified and identified on the basis of analysing and determining the structure of convolutional neural network. The experimental results show that the accuracy of defect detection reaches 95%, the false alarm rate is only 5%, and the recognition speed is fast, which achieves the effective recognition of small diameter pipe X-ray weld images.

关键词： Training image segmentation image recognition Accuracy Welding Signal processing algorithms Feature extraction Convolutional neural networks X-ray imaging Defect detection

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Autofocus optical imaging system based on image processing 14

Autofocus optical imaging system based on image processing

引用

14th International conference on Information Optics and Photonics, CIOP 2023

作者： Teng, Teng Chen, Guan xiaofei, Wang Hua, Fan Dongjie, Ju Zhaojuan, Chi Jinan250000 China Shandong JINGWEI Intelligent Equipment Co. Ltd Weifang261000 China

ISBN: (纸本)9781510671744

Autofocus optical imaging systems are widely used in industrial inspection, medical diagnosis, drone photography, machine vision and other fields. image-based autofocus algorithms typically consist of two key steps: image sharpness evaluation and search strategy. In this study, we propose an advanced image-based autofocus algorithm specifically designed for industrial image measurement. In order to improve the accuracy of the clarity assessment, we improve the existing method by introducing Gaussian filtering and threshold controlled Laplace operators. This improvement effectively reduces the effects of noise and light intensity changes, improving the reliability of clarity measurements. In addition, we propose a novel distance adjustment strategy combining coarse and fine tuning as part of the search strategy. This strategy reduces the interference of local peaks, allowing the algorithm to accurately identify the best image focus. The proposed image-based autofocus algorithm has several advantages, e.g. high focusing accuracy, high repeatability, and stability under light intensity changes2. These advantages make it ideal for industrial image measurement applications. To verify the effectiveness of our method, we build an experiment setup in a lab using ADLINK PCI-9114-DG board and a custom-designed lens. The results show that the sensitivity and performance of the autofocus system meet certain requirements. In summary, the image-based autofocus algorithm that we developed enables accurate and reliable focusing. It overcomes some challenges such as noise and light intensity changes to ensures optimal image focus, and improves overall image quality. The successful implementation of our autofocus system can benefit industrial inspection, medical diagnostics and many other applications. © 2023 SPIE.

关键词： Diagnosis

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A DOPPLER SPECTRUM FOLDING REMOVAL METHOD FOR HIGH RESOLUTION SQUINTED SAR

A DOPPLER SPECTRUM FOLDING REMOVAL METHOD FOR HIGH RESOLUTIO...

引用

IET International Radar conference 2023, IRC 2023

作者： Gao, Yang xiang, Tianshun Yang, Juanjuan Dang, Hongxing Lei, Suli Tan, xiaomin China Academy of Space Technology Xi'an China

ISBN: (纸本)9781839539954

With the increase of resolution, SAR systems operate on wider bandwidth. When SAR works with squint angle, signals of different frequencies will generate different Doppler offset at the same squint angle. As the squint angle increases, the Doppler variations caused by different signal frequencies become more significant. Besides the Doppler bandwidth corresponding to the beam width, the variation changes the position and the distribution of the original Doppler band. This problem causes the extension of Doppler bandwidth and results in the Doppler spectrum folding. For high-resolution SAR with squint angle, this paper proposes a pre-processing method to overcome the problem of Doppler spectrum folding which leads to the image quality degradation. The method contains Doppler spectrum reshaping and support domain extension. In this paper, the processing procedure of the method are presented and the effectiveness of this method is verified by simulation results and the real data results. © The Institution of Engineering & Technology 2023.

关键词： Gluing

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Color image Encryption Algorithm Based on High Dimensional Combination Chaotic System and DNA Coding 4

Color Image Encryption Algorithm Based on High Dimensional C...

引用

4th International conference on Natural Language processing, ICNLP 2022

作者： Wang, Peisen Zhang, Xuefeng Huang, Jiaxiang Xi'an University of Posts and Telecommunications School of Cyberspace Security Xi'an710121 China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

In order to solve the problem of limited key space and simple chaotic behavior of a single chaotic system, a high dimensional combined chaotic system model based on cosine transform is proposed. By combining one dimensional chaotic systems with weights, the output results are coupled by cosine transform, and then the corresponding high dimensional combined chaotic system is obtained. A color image encryption algorithm based on high-dimensional combined chaotic system and DNA encoding is designed according to the proposed high-dimensional combined chaotic system. The algorithm first uses the sequence value generated by the high-dimensional combined chaotic system to scramble the plaintext image at the bit and pixel levels. Secondly, DNA encoding technology is used to perform dynamic DNA operation on the scrambled image. Finally, the nonlinear component S-box is used to replace pixel values, which further improves the security of the algorithm. The simulation results show that the combined chaotic system has larger key space and better chaotic behavior, and the color image encryption algorithm based on high dimensional combined chaotic system and DNA can resist various common attack methods and has better security. © 2022 IEEE.

关键词： Chaotic systems

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Research on the application of transformer in computer vision

Research on the application of transformer in computer visio...

引用

2023 International conference on Mechatronics and Smart systems, CONF-MSS 2023

作者： Bai, Guoli Guo, Haosen xiao, Chuzhen School of Information Science and Technology Bohai University Jinzhou121000 China School of Computer Science Xi'an University of Posts and Telecommunications Xi'an710000 China School of Computer Science Hunan University of Technology and Business Changsha410000 China

The Transformer is a deep neural network model that utilizes attention mechanisms to improve model performance. Initially, the Transformer gained significant attention in the field of natural language processing. In recent years, due to continuous improvements and extensions to the Transformer model structure, it has also achieved many important breakthroughs in computer vision(CV) tasks, attracting the interest of many researchers. However, there is a lack of comprehensive review articles on the application and development of the Transformer in computer vision. A summary of the Transformer's applications and advancements in computer vision is given in this paper. It discusses the Transformer model's fundamental ideas and organizational framework, and primarily introduces its applications in various fields such as image classification, object detection, and image generation, as well as the superiority of the Transformer+ convolutional neural network(CNN) fusion model. The paper provides a detailed analysis of classic models such as Vision Transformer(ViT), Detection Transformer(DETR) and discusses their strengths, weaknesses, and improvement methods. Finally, the paper summarizes and looks forward to the Transformer's evolution in computer vision. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Object detection

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Research on the processing algorithm for solar cell characterization data 2

Research on the processing algorithm for solar cell characte...

引用

2nd International conference on Electronic Information Engineering and Computer Communication, EIECC 2022

作者： Gui, Ruoxia Cheng, Jin Li, Junming Zhang, Chunqian Qin, Lei Research Center for Sensor Technology Beijing Key Laboratory for Sensor School of Applied Sciences Beijing Information Science and Technology University Jianxiangqiao Campus Beijing100101 China Beijing Key Laboratory for Optoelectronic Measurement Technology Beijing Information Science & Technology University Beijing100192 China

ISBN: (纸本)9781510663121

The research of solar cells has gone through three generations, silicon solar cells, thin film solar cells and perovskite solar cells. There are a variety of characterization methods used to analyze the factors that affect the performance of solar cells. Thus far, there are some deficiencies in the research of algorithms of the characterization methods themselves. Three types of data: current density-voltage (J-V) curves, X-ray diffraction (XRD) spectrum and absorption spectrum are the most essential characterization methods. Here, the processing algorithms of these three kinds of characterization data are studied, intend to help researchers to solve the practical problems more efficiently, and improve the accuracy of experimental results. In this paper, the process of processing algorithm are summarized in the way of flow chart, and combined with the example to show in turn. © 2023 SPIE.

关键词： Silicon solar cells

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Diagnosis of Pneumonia using image Recognition Techniques 7

Diagnosis of Pneumonia using Image Recognition Techniques

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7th International conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Sangeetha, S. Suruthika, S. Keerthika, S. Vinitha, S. Sugunadevi, M. Kongunadu College of Engineering and Technology Department of IT Tamil Nadu Trichy India

ISBN: (纸本)9798350397253

Pneumonia is a severe and fatal disease caused by viral or bacterial sepsis in human lungs. Early detection of pneumonia is critical for successful treatment. Deep learning algorithms that use many layers to interpret a boundary of data have obtained the best results in a variety of disciplines, particularly in the documenting and sorting of various diseases. As a result, there is an increasing need to design and develop a pneumonia detection system by using deep learning methods with an ability to evaluate chest CT-scan imageries and aid in the pneumonia exposure process. The proposed framework contains three phases: feature extraction, image classification, and image pre-processing. As a consequence, the projected Compared to other deep-learning models that have already been trained, such as (ResNet 50) and SVM, pneumonia diagnosis using the CNN model performed better in terms of accuracy and reliability. As a result, the efficient performance of the proposed Convolutional Neural Network centred pneumonia perception model in every parameter can provide valuable assistance to patients' care and reduce the human error rates. © 2023 IEEE.

关键词： Convolutional neural networks

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