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Analysis of Quantum-Classical Hybrid deep learning for 6G image processing with Copyright Detection

INFORMATION 2024年第11期15卷 727页

作者： Seol, Jongho Kim, Hye-Young Kancharla, Abhilash Kim, Jongyeop Middle Georgia State Univ Dept Comp Sci Warner Robins GA 31093 USA Hongik Univ Sch Games Game Software Seoul 121791 South Korea Univ Tampa Dept Comp Sci Tampa FL 33606 USA Georgia Southern Univ Dept Informat Technol Statesboro GA 30458 USA

This study investigates the integration of quantum computing, classical methods, and deep learning techniques for enhanced image processing in dynamic 6G networks, while also addressing essential aspects of copyright technology and detection. Our findings indicate that quantum methods excel in rapid edge detection and feature extraction but encounter difficulties in maintaining image quality compared to classical approaches. In contrast, classical methods preserve higher image fidelity but struggle to satisfy the real-time processing requirements of 6G applications. deep learning techniques, particularly CNNs, demonstrate potential in complex image analysis tasks but demand substantial computational resources. To promote the ethical use of AI-generated images, we introduce copyright detection mechanisms that employ advanced algorithms to identify potential infringements in generated content. This integration improves adherence to intellectual property rights and legal standards, supporting the responsible implementation of image processing technologies. We suggest that the future of image processing in 6G networks resides in hybrid systems that effectively utilize the strengths of each approach while incorporating robust copyright detection capabilities. These insights contribute to the development of efficient, high-performance image processing systems in next-generation networks, highlighting the promise of integrated quantum-classical-classical deep learning architectures within 6G environments.

关键词： copyright detection quantum computing hybrid deep learning image processing 6G networks

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Research on License Plate Character Recognition Technology Based on image processing and deep learning

Research on License Plate Character Recognition Technology B...

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IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)

作者： Chen, Chun Zhong, Xiaolei Honghe Vocat & Tech Coll Honghe 661100 Peoples R China Yunnan Univ Dianchi Coll Kunming 650228 Yunnan Peoples R China

ISBN: (纸本)9781665416061

Character recognition methods are applied in many fields, greatly improving work efficiency in daily life[1], such as license plate retrieval, invoice printing recognition, lottery betting codes, tax reports, etc. Digital recognition has been widely used in the field of computer vision and image recognition, and deep learning algorithms are currently popular image recognition algorithms. deep learning has been widely studied and applied in target recognition and speech content recognition. With the rapid increase in production requirements and computer data processing speed, the application of character recognition in actual production and life is becoming more and more common[2]. It is also extremely important for automatic retrieval and real-time, fast and accurate character input. However, traditional pattern recognition and feature extraction algorithms cannot well meet the requirements of real-time and correctness in production. At the same time, due to the vigorous development of deep learning, character recognition technology based on deep learning has advantages that traditional recognition algorithms cannot match. This paper proposes a barcode recognition algorithm based on a deep neural network combined with a global optimization method. It uses a convolutional recurrent network to extract the characteristics of each character in the barcode and classify it. Compared with the traditional method, it has stronger adaptability and generalization. Chemical energy.

关键词： deep learning character recognition recognition algorithms

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Automatic Volume Control Using image processing and deep learning Techniques: A Review

Automatic Volume Control Using Image Processing and Deep Lea...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Kinshuk Theraja Sakshi Gupta deepak Kumar Gupta Amity Institute of Information and Technology Amity University Noida

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

This paper presents an innovative approach to automatic volume control using image processing and deep learning techniques. The ability to automatically adjust volume levels based on environmental factors and user preferences has significant implications for various audio applications, including teleconferencing systems, smart devices, and public address systems. By combining image processing algorithms with deep learning models, this paper aims to develop a robust and adaptive volume control system capable of accurately adjusting audio levels in real-time. The paper discusses the theoretical foundations, technical implementation, experimental results, and potential applications of the proposed automatic volume control system.

关键词： deep learning Visualization Teleconferencing Solid modeling image processing Software algorithms Process control

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Utilizing deep learning and image processing for Detection of Pests 2

Utilizing Deep Learning and Image Processing for Detection o...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Santhosh, B.J. Adithya, H.R. Akash Kumar, G.S. Hemanth Gowda, R.M. Jaswanth, H.V. BGS Institute of Technology Adichunchanagiri University India 20CSE001 Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University India 20CSE004 Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University India 20CSE024 Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University India 20CSE032 Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri university India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Pests are one of the biggest hazards to crops since they significantly reduce the amount of food produced. Furthermore, by enabling farmers to take the necessary preventive measures, early and accurate pest identification can help minimize damage and improve crop quality. Inspection takes a lot of time and labour because many pest species seem to be identical to one another. This work presents a novel deep-learning technique called Faster-Pest Net, which aims to overcome the limitations of physical pest inspection. Using the Mobile Net as its foundation network, adescriptively enhanced Faster-RCNN technique is created, calibrated on pest samples to identify crop pests across many categories, and dubbed Faster- PestNet. ©2024 IEEE.

关键词： Adversarial machine learning

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Characterization of the in-focus droplets in shadowgraphy systems via deep learning-based image processing method

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PHYSICS OF FLUIDS 2022年第11期34卷 1-15页

作者： Wang, Zhibo He, Feng Zhang, Haixiang Hao, Pengfei Zhang, Xiwen Li, Xiangru Tsinghua Univ Dept Engn Mech Appl Mech Lab Beijing 100084 Peoples R China Tsinghua Univ Sch Mat Sci & Engn AV Aerodynam Res Inst Joint Res Ctr Adv Mat & Antiicing Beijing 100084 Peoples R China

It is important to accurately identify and measure in-focus droplets from shadowgraph droplet images that typically contain a large number of defocused droplets for the research of multiphase flow. However, conventional in-focus droplet identification methods are time-consuming and laborious due to the noise and background illumination in experimental data. In this paper, a deep learning-based method called focus-droplet generative adversarial network (FocGAN) is developed to automatically detect and characterize the focused droplets in shadow images. A generative adversarial network framework is adopted by our model to output binarized images containing only in-focus droplets, and inception blocks are used in the generator to enhance the extraction of multi-scale features. To emulate the real shadow images, an algorithm based on the Gauss blur method is developed to generate paired datasets to train the networks. The detailed architecture and performance of the model were investigated and evaluated by both the synthetic data and spray experimental data. The results show that the present learning-based method is far superior to the traditional adaptive threshold method in terms of effective extraction rate and accuracy. The comprehensive performance of FocGAN, including detection accuracy and robustness to noise, is higher than that of the model based on a convolutional neural network. Moreover, the identification results of spray images with different droplet number densities clearly exhibit the feasibility of FocGAN in real experiments. This work indicates that the proposed learning-based approach is promising to be widely applied as an efficient and universal tool for processing particle shadowgraph images.

关键词： Generative adversarial networks

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Multi-Field Distributions Generation for Complex Objects Using a deep learning-Based image processing Method

Multi-Field Distributions Generation for Complex Objects Usi...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Wu, Chenhao Yang, Shuojin Huang, Zhongwei Zou, Yuntao Liu, Jiang Graduate School of Fundamental Science and Engineering Waseda University Tokyo Japan Tsinghua University Department of Computer Science and Technology Beijing China Macau University of Science and Technology Faculty of Innovation Engineering China School of Energy and Power Engineering Huazhong University of Science and Technology Hubei China

ISBN: (纸本)9798350378214

Accurately and rapidly determining the field distribution for complex objects is crucial, especially in the energy sector. Computational Fluid Dynamics (CFD) methods have been employed to simulate thermal field flows by numerically solving the Navier-Stokes (NS) equations. This methodology requires the implementation of complex procedures, such as the establishment of the computational domain, the discretization of the partial differential equation, and so forth. Consequently, it is both time-consuming and labor-intensive. This paper presents a novel approach for predicting thermal field distributions using a deep learning-based image processing method. Generative Adversarial Networks (GANs), which are known for their ability to generate complex images, present a new opportunity for simulating thermal field distributions. In contrast to conventional CFD techniques, GANs are capable of directly generating thermal field distribution images from input object images without the necessity of solving NS equations, thereby significantly simplifying the process. The approach is based on the StarGAN v2 architecture, which has been shown to be efficient for generating complex images. The method is applied to a dataset constructed using traditional CFD methods, and the results demonstrate that the generated thermal field images closely match the original images, with a Structural Similarity Index (SSIM) value exceeding 0.92, this confirms the effectiveness and precision of the proposed method. © 2024 IEEE.

关键词： Navier Stokes equations

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Review of Modern Forest Fire Detection Techniques: Innovations in image processing and deep learning

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INFORMATION 2024年第9期15卷 538页

作者： Ozel, Berk Alam, Muhammad Shahab Khan, Muhammad Umer Atilim Univ Dept Mechatron Engn TR-06830 Ankara Turkiye Gebze Tech Univ Def Technol Inst TR-41400 Gebze Turkiye

Fire detection and extinguishing systems are critical for safeguarding lives and minimizing property damage. These systems are especially vital in combating forest fires. In recent years, several forest fires have set records for their size, duration, and level of destruction. Traditional fire detection methods, such as smoke and heat sensors, have limitations, prompting the development of innovative approaches using advanced technologies. Utilizing image processing, computer vision, and deep learning algorithms, we can now detect fires with exceptional accuracy and respond promptly to mitigate their impact. In this article, we conduct a comprehensive review of articles from 2013 to 2023, exploring how these technologies are applied in fire detection and extinguishing. We delve into modern techniques enabling real-time analysis of the visual data captured by cameras or satellites, facilitating the detection of smoke, flames, and other fire-related cues. Furthermore, we explore the utilization of deep learning and machine learning in training intelligent algorithms to recognize fire patterns and features. Through a comprehensive examination of current research and development, this review aims to provide insights into the potential and future directions of fire detection and extinguishing using image processing, computer vision, and deep learning.

关键词： artificial intelligence deep learning detection fire flame forest fire smoke wildfire

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deep learning-based medical image processing of lung CT 4

Deep learning-based medical image processing of lung CT

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4th IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Pan, Zeyu Shenzhen University College of Physics and Optoelectronic Engineering Shenzhen China

ISBN: (纸本)9798350361643

Aiming at the planning and navigation needs in lung disease surgery, and the time-consuming and laborious manual labeling of lung CT arterial segmentation, a 3DUNet architecture neural network incorporating CBAM attention mechanism is proposed for fast extraction of lung arterial veins. Based on the 2DUNet architecture, the dimension of the processed image is upgraded from two-dimensional to three-dimensional 3DUNet, and the CBAM attention module is added before pooling or up-convolution at the end of each convolution layer, and the parameter update is performed by using the Focal Loss loss function combined with stochastic gradient descent method. A three-fold cross-validation was performed using 95 lung CT data and ablation experiments were performed. After experimental validation, the model with the addition of CBAM attention module and Focal Loss loss function showed significant improvement in segmentation metrics, in which the accuracy, sensitivity, Dice coefficient, and precision for arteries and veins reached 99.8% and 99.8%, 84.9% and 87.2%, 82.5% and 84.8%, 81.1% and 83.5%, respectively. © 2024 IEEE.

关键词： Convolution

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Adaptive image processing embedding to make the ecological tasks of deep learning more robust on camera traps images

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ECOLOGICAL INFORMATICS 2024年 82卷

作者： Yang, Zihe Tian, Ye Zhang, Junguo Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Key Lab State Forestry & Grassland Adm Forestry E Beijing 100083 Peoples R China Beijing Forestry Univ Res Ctr Biodivers Intelligent Monitoring Beijing 100083 Peoples R China

Camera traps serve as a valuable tool for wildlife monitoring, generating a vast collection of images for ecologists to conduct ecological investigations, such as species identification and population estimation. However, the sheer volume of images poses a challenge, and the integration of deep learning into automated ecological investigation tasks remains complex, particularly when dealing with low-quality images in long-term monitoring programs. Existing approaches often struggle to strike a balance between image enhancement and deep learning for ecological tasks, thereby overlooking crucial information contained within low-quality images. This research introduces a pioneering adaptive image processing module (AIP) that seamlessly incorporates image processing into camera trap ecological tasks, elevating the performance of wildlife monitoring activities. Specifically, a differentiable image processing (DIP) module is presented to enhance low-quality images, with its parameters predicted by a Non-local based parameter predictor (NLPP). Additionally, an end-to-end approach based on hybrid data containing both original and synthetic data is proposed, encompassing adaptive image processing methods and downstream tasks for camera traps, adaptable to various scenarios. This approach effectively reduces the manual labor and time required for professional image processing. When applied to real-world camera trap images and synthetic image datasets, our method achieves an accuracy of 92.26% and 86.65% in classifying wildlife, respectively, demonstrating its robustness. By outperforming alternative methods under harsh conditions, the application of the adaptive image processing module instills greater confidence in deep learning applications within complex environments.

关键词： Adaptive image processing Camera traps deep learning Ecological tasks

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deep learning Methods of Satellite image processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

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WATER 2024年第8期16卷 1141页

作者： Lemenkova, Polina Univ Salzburg Fac Digital & Analyt Sci Dept Geoinformat Schillerstr 30 A-5020 Salzburg Austria

Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world's largest river delta and is prone to floods that impact social-natural systems through losses of lives and damage to infrastructure and landscapes. Millions of people living in this region are vulnerable to repetitive floods due to exposure, high susceptibility and low resilience. Cumulative effects of the monsoon climate, repetitive rainfall, tropical cyclones and the hydrogeologic setting of the Ganges River Delta increase probability of floods. While engineering methods of flood mitigation include practical solutions (technical construction of dams, bridges and hydraulic drains), regulation of traffic and land planning support systems, geoinformation methods rely on the modelling of remote sensing (RS) data to evaluate the dynamics of flood hazards. Geoinformation is indispensable for mapping catchments of flooded areas and visualization of affected regions in real-time flood monitoring, in addition to implementing and developing emergency plans and vulnerability assessment through warning systems supported by RS data. In this regard, this study used RS data to monitor the southern segment of the Ganges River Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated in flood (March) and post-flood (November) periods for analysis of flood extent and landscape changes. deep learning (DL) algorithms of GRASS GIS and modules of qualitative and quantitative analysis were used as advanced methods of satellite image processing. The results constitute a series of maps based on the classified images for the monitoring of floods in the Ganges River Delta.

关键词： machine learning deep learning flood monsoon cartography geoinformatics remote sensing satellite image geospatial analysis GRASS GIS

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