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检索条件"任意字段=2nd Conference on Image Reconstruction from Incomplete Data"

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image Recognition Algorithm of UAV Inspection under Machine Vision 2

Image Recognition Algorithm of UAV Inspection under Machine ...

引用

2nd IEEE International conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Hu, Chenlu Mo, Zirui Zhang, Zhonghuizi Qiu, Xincheng Shihezi University School of Mechanical and Electrical Engineering Shihezi China University of Manchester School of Engineering Manchester United Kingdom

ISBN: (纸本)9798350317558

UAVs have the advantages of efficient and automated inspection in life, and have important application value in industry, construction, energy and other fields. In this paper, an improved image recognition algorithm based on machine vision is proposed to solve the problems existing in UAV inspection. Through the use of computer vision technology, this paper analyzes and processes the images captured by unmanned aerial vehicles to complete the automatic recognition and defect detection of objects. A complete algorithm system composed of image preprocessing, feature extraction, object classification, and defect recognition is also proposed. Using large-scale drone patrol image data, the method is evaluated from three aspects: accuracy, F1 value and recognition speed. After testing, the prediction accuracy of this method can reach 89% to 96%. The algorithm has made significant improvements in target recognition and defect detection. The improvement in accuracy and F1 score indicates that the algorithm can identify the target. © 2024 IEEE.

关键词： Drone Inspection image Recognition Machine Vision SIFT Algorithm

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Efficient Diabetic Retinopathy Segmentation and Classification using Residual Encoder-Decoder Architecture 2

Efficient Diabetic Retinopathy Segmentation and Classificati...

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2nd International conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Betha, Sashi Kanth Seventline, J. B. GITAM Deemed Be Univ Visakhapatnam Andhra Pradesh India Vignans Inst Engn Women Dept ECE Visakhapatnam Andhra Pradesh India GITAM Deemed Be Univ Dept EECE Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9798350391558;9798350379990

Diabetic retinopathy (DR), a severe complication arising from diabetes, poses a significant threat to vision due to the deterioration of retinal vessels. Recent techniques in DR detection, such as Convolutional Neural Networks (CNNs) and deep learning models, have shown promise but face challenges in accurately segmenting and classifying retinal images due to variations in image quality, occlusions, and the need for large annotated datasets. This study presents an innovative methodology for automated detection, grading, and segmentation of DR using deep learning, with a focus on residual encoder-decoder architecture. The study utilizes the Indian Diabetic Retinopathy image dataset (IDRID), comprising 81 fundus images and labels, to rigorously evaluate the proposed methodology. By employing advanced image preprocessing techniques to enhance data quality, followed by a unified model capable of both segmentation and classification tasks, the proposed method achieves competitive performance metrics. Specifically, the model demonstrates an accuracy of 85.2% and specificity of 86.1% in segmenting and classifying DR features. These findings contribute to the improvement of diagnostic accuracy and patient outcomes in retinal diseases, offering potential applications in clinical settings to support early diagnosis and management of DR, thereby enhancing patient care and alleviating healthcare system burdens.

关键词： Diabetic retinopathy Residual encoder decoder image Pre-processing Segmentation Grading

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AquaVision Dehaze and Enhancement: A Robust Algorithm for Improving Underwater image Quality in Aquatic Environments 2

AquaVision Dehaze and Enhancement: A Robust Algorithm for Im...

引用

2nd International conference on Computer Vision and Internet of Things, ICCVIoT 2024

作者： Vijayalakshmi, M. Sasithradevi, A. Spoorthi, J.S. Prakash, P. School of Electronics Engineering Vellore Institute of Technology Chennai India Centre for Advanced Data Science Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Electronics Engineering Madras Institute of Technology Chennai India

ISBN: (数字)9781837242535

ISBN: (纸本)9781837242672

Underwater imaging is frequently challenged by light scattering, leading to haze, color distortion, and visibility loss. To address these issues, we introduce the AquaVision Dehaze and Enhancement algorithm to improve underwater image quality. Extensive testing on a dataset from an aquaculture environment demonstrates that the algorithm significantly enhances image clarity. Quantitative evaluations reveal high performance across various image quality metrics, with scores of UICM (19.6463), UISM (18.1490), UIConM (26.7463), and UIQM (13.3312). Furthermore, the algorithm delivers competitive results in no-reference image quality metrics, including NIQE (3.6780), Entropy (7.5942), PIQE (52.1444), and BRISQUE (35.9847). These results highlight the algorithm's effectiveness in addressing the challenges of underwater imaging and illustrate the effectiveness through an ablation study. The AquaVision Dehaze and Enhancement algorithm offers a robust solution for improving the quality making it a valuable tool for advancing research in aquatic environments. © The Institution of Engineering & Technology 2024.

关键词： Aquaculture

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Blind image De-Blurring with PNN and Random Forest Regression Model 2

Blind Image De-Blurring with PNN and Random Forest Regressio...

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2nd International conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Gopal, Dhanalakshmi Kumar Devulapalli, Praveen Shepuri, Srinivasulu Zabih, Mohd Babu Maganti, Sushanth Avn Institute of Engineering and Technology Department of Electronics and Communication Engineering Hyderabad India Vardhaman College of Engineering Department of Electronics and Communication Engineering Hyderabad India Chaitanya Bharathi Institute of Technology Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350382952

image restoration is a very difficult task that always needs kernel knowledge beforehand, but blindly blurred images like camera-shake don't have any clue of the blur kernel or the original image. Restoring these images is found challenging and requires high data to move on, Modern performances have been attained by various models using a variety of priors but most of all are lagging in some or other contexts. In this study, we suggest a novel tactic called the Adaptive Technique for Blind image Deblurring (AMBID). This method follows the kernel prediction and image restoration where the model adopts itself for the best kernel match. The model uses the technologies like Probabilistic Neural Network, Hough Transform, Random Forest Regression model along with the help of image parameters and percentage of parameters from colour image to Gray scale. Experimental findings demonstrate that the proposed brand-new adoptive blind deblurring method in this work obtained the average value of PSNR of 40.095dB. The majority of picture metrics, such as variance, will decrease following the blurring procedure, which serves as the inspiration for our work. Our work has shown that the output quality of the proposed approach is better than the GibbsDDRM and Groud Truth approaches. © 2024 IEEE.

关键词： image reconstruction

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Proceedings - ICNEWS 2024: 2nd International conference on Networking, Embedded and Wireless Systems: Wireless Technology - Building a Digital World

Proceedings - ICNEWS 2024: 2nd International Conference on N...

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2nd International conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

ISBN: (纸本)9798350367461

The proceedings contain 75 papers. The topics discussed include: analysis of mental health disorders from survey reports using time series based linear regression;performance analysis of an energy efficient 1-bit hybrid full adder;KisanConnect: reshaping agricultural trade with dynamic pricing model;design of a 10-bit potentiometric DAC using Sky130nm technology using Xschem & Ngspice;quantum-dot cellular automata technology to implement digital circuits;advanced solar-powered oceanic environment monitoring buoy: real-time data for a sea conditions;real-time snake detection and recognition systems in videos using deep learning;a deep learning-based stream CipherGenerator for medical image encryption and decryption;image segmentation for MRI brain tumor detection using advance ai algorithm;and a comprehensive step of XAI for Bengali and English text detection and recognition from natural scene images.

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An Investigation of image Security and image Steganography Techniques 2

An Investigation of Image Security and Image Steganography T...

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2nd International conference on Advances in Computational Intelligence and Communication, ICACIC 2023

作者： Thangaraju, Dineshkumar Asokan, R. Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798350318456

In recent days, data transmission has increased a lot and they demand high security protocols for the safe transmission of data. Among the different types of data, images are sensitive as they could carry or represent personal information including medical reports. To transmit images in a safe way, there has been a diverse collection of works in literature. The objective of this paper is to investigate the existing works in terms of image security achieved through cryptographic techniques and steganographic techniques. image security achieved using cryptographic techniques focus on non-retrievability of the original image from the stego image whereas steganographic techniques focus on hiding the secret information inside a cover medium. We have also recorded the metrics of the different works. © 2023 IEEE.

关键词： Steganography

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Violent Behaviour Analysis in Crowd 2

Violent Behaviour Analysis in Crowd

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2nd International conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Ganga, B. Lata, B.T. Pallavi, T.R. Venugopal, K.R. Uvce Bangalore University Dept.of Cse Bengaluru India

ISBN: (纸本)9798350328202

In response to the increasing demand in crowd management, and to solve the challenges of categorizing crowds as violent and non-violent, we have introduced a new architecture called Violent Behaviour Analysis (VBA). The VBA architecture combines the Congested Scene Recognition Network (CSRNet) and the Unet, CSRNet consists of a data acquisition module in the foreground and a processing unit in the background. The data acquisition module captures intricate crowd-related details and the processing unit performs the generation of a density map for precise spatial representation. The Unet along with CSRNet estimates density maps and also distinguishes crowd regions from their backgrounds to improve accuracy. The architecture is evaluated using a video dataset known as the Violent dataset built from an image dataset of fine-grain crowd-counting. The results with density maps and CMAE demonstrate the efficiency of this approach in categorizing the crowd as violent and violent individuals. © 2024 IEEE.

关键词： data acquisition

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Advancements in image Fusion Techniques for IoT Systems: Current Trends and Applications 2

Advancements in Image Fusion Techniques for IoT Systems: Cur...

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2nd International conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Sharma, Shweta Rani, Shalli Sharma, Ankita Dogra, Ayush Chitkara Univ Inst Engn & Technol Rajpura 140401 Punjab India

ISBN: (纸本)9798331540661;9798331540678

This research explores the transformative potential of integrating image fusion with the Internet of Things (IoT) in the field of smart healthcare. By combining multiple images from various sources, image fusion provides richer and more precise data, enhancing the capabilities of IoT devices. This synergy optimizes data quality and reliability in IoT systems, leading to improved diagnosis, patient monitoring, and telemedicine services in healthcare. The paper investigates the approaches and technologies used in combining image fusion with IoT, addressing challenges and limitations. Through case studies and current implementations, we highlight the transformative potential of this integration in creating smarter, more responsive, and efficient healthcare systems. Additionally, this study explores the implications for other sectors such as environmental monitoring, security, and smart cities.

关键词： image Fusion IoT Remote Sensing GAN ML

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An Overview of Moving Object Detection Using YOLO Deep Learning Models 2

An Overview of Moving Object Detection Using YOLO Deep Learn...

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2nd International conference on Disruptive Technologies, ICDT 2024

作者： Dwivedi, Upendra Joshi, Kireet Shukla, Surendra Kumar Rajawat, Anand Singh G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India SVKM S Nmims Mpstme Department of Computer Science and Engineering Shirpur Campus Shirpur India School of Computer Sciences and Engineering Sandip University Nashik India

ISBN: (纸本)9798350371055

Computer vision is a promising domain that focuses on emerging approaches, algorithms and technologies to provide computing capability to machine to analysis visual data, such as image files, videos files and real time video streaming. In Computer Vision and image processing detecting object from images and videos has been topic of extensive research. This is accomplished by applying various computer vision techniques to analyze the visual data and determine class of objects from image and videos files. One widespread approach to object detection is using the deep learning models. YOLO (You Only Look Once) is a convolutional neural network that offers a fast and efficient solution to object detection using deep learning. It enable computer to handle real-time detection of objects in video frames and accurately locate and classify moving objects. YOLO is able to simultaneously detect and classify objects in an efficient manner using convolutional neural networks. © 2024 IEEE.

关键词： Deep Learning image Processing Motion Detection Moving Object Detection YOLO

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Classification of Cashew Nut Pests and Diseases based on EfficientNetv2-S 2

Classification of Cashew Nut Pests and Diseases based on Eff...

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2nd International conference on Machine Vision, image Processing and Imaging Technology, MVIPIT 2024

作者： Liu, Ran Yang, Haiyang Zhang, Xiaotian Yang, Jingjing School of Information Science and Engineering Hebei North University Zhangjiakou China

ISBN: (纸本)9798331543037

Cashew kernel has rich nutritional value, cashew shell has very high industrial value, cashew shell is rich in cashew shell oil has high economic value of natural resources. However, cashews are suffering from pests and diseases, resulting in reduced production. Therefore, testing for cashew diseases and pests is crucial to avoid cashew loss. In this study, deep learning model and machine learning model were combined to detect cashew leaf pests and diseases, feature extraction was performed using EfficientNetv2-S, and classification was performed by XGBoost classifier. The visualization technology Grad-CAM was used to generate heat maps for the final convolutional layer of the feature extraction model, analyze the less concerned part of the feature extraction model, and further modify the feature extraction *** our research, a public dataset is used, and the data enhancement operation is carried out on the basis of this dataset. The presented classification algorithm was tested on this data set. The results indicate that the algorithm achieves high accuracy in the object detection task. ©2024 IEEE.

关键词： Deep learning

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