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Measuring Surface Characteristics in Sustainable Machining of Titanium Alloys Using deep learning-Based image processing

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IEEE SENSORS JOURNAL 2023年第12期23卷 13629-13639页

作者： Ross, Nimel Sworna Shibi, C. Sherin Mustafa, Sithara Mohamed Gupta, Munish Kumar Korkmaz, Mehmet Erdi Sharma, Vishal S. Li, Z. SIMATS Saveetha Sch Engn Dept Mech Engn Chennai India SRM Inst Sci & Technol Dept Computat Intelligence Kattankulathur Tamil Nadu India Inst Appl Technol Appl Engn Dept Mech Engn Ajman U Arab Emirates Opole Univ Technol Fac Mech Engn Opole Poland Karabuk Univ Karabuk Turkiye Dr BR Ambedkar Natl Inst Technol Ind & Prod Engn Jalandhar India

A crucial method of maintenance in the manufacturing industry is machine vision-based fault diagnostics and condition monitoring of machine tools. The friction that occurs between the tool and the workpiece has a greater influence on the surface properties of the material. Effective problem diagnosis is necessary for machine systems to continue operations safely. Data-driven approaches have recently exhibited great promise for intelligent fault diagnosis. Unfortunately, the data collected under real-world conditions may be imbalanced, making diagnosis difficult. In dry, minimum quantity lubrication (MQL), and cryogenic circumstances, the method of failure detection of the proposed design is novel. The purpose of this interrogation is to evaluate the roughness profiles obtained from the machined surfaces and class separation. Markov transition field (MTF) is adopted to encode the surface profiles. In addition to this, conditional generative adversarial network (CGAN) for augmentation and bidirectional long-short term memory (BLSTM), multilayer perceptron (MLP), and 2-D-convolutional neural network (CNN) models are used for surface profile classification and correlation with process parameters. According to the study's finding, the 2-D-CNN was significantly more accurate than the models in predicting surface profiles, with an average accuracy of above 99.6% in both training and testing. In the limelight, the suggested approach can demonstrate to be quite useful for categorizing and proposing appropriate machining circumstances, specifically in situations with minimal data.

关键词： Surface treatment Machining Surface morphology Markov processes Surface roughness Rough surfaces Predictive models Conditional generative adversarial network (CGAN) cryogenic deep learning (DL) machining Markov transition field (MTF)

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

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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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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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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 with image processing for Object Detection in Traffic Cameras

Deep Learning with Image Processing for Object Detection in ...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Santhosh D S. Prabu Department of Computer Applications Hindustan Institution of Technology and Science Chennai Tamil Nadu

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Traffic surveillance is a key factor in ITS whereby accurate and real-time object detection assures improvement of road safety and traffic management. This paper advances a deep-learning-based perspective that combines image processing techniques with convolutional neural networks (CNNs) to maximize the object detection accuracy over the traffic camera feed. The filtering, edge detection, and feature extraction of images for data preprocessing enhance the model performance. This setup provides high inference speed with reliable detection of vehicles via YOLO (You Only Look Once) and Faster R-CNN. The obtained experimental results from our evaluation show great enhancement in the detection accuracy which places the model in a position for real-time application in traffic surveillance. The study also investigates the computational efficiency and real-life boundaries for implementing this strategy, providing an exhaustive account of the suitability of the proposed method in larger-scale applications in ITS.

关键词： YOLO deep learning Computer vision Accuracy Surveillance Computational modeling Traffic control Cameras real-time systems Convolutional neural networks

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Three-dimensional measurement of the droplets out of focus in shadowgraphy systems via deep learning-based image-processing method

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PHYSICS OF FLUIDS 2022年第7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 AV Aerodynam Res Inst Joint Res Ctr Adv Mat & Anti Sch Mat Sci & Engn Beijing 100084 Peoples R China

For the shadowgraphy techniques with a single camera, it is difficult to accurately obtain the shape, size, and depth location of the droplets out of focus due to the defocus blur. This paper proposed a deep learning-based method to recover the sharp images and infer the depth information from the defocused blur droplets images. The proposed model comprising of a defocus map estimation subnetwork and a defocus deblur subnetwork is optimized with a two-stage strategy. To train the networks, the synthetic blur data generated by the Gauss kernel method are utilized as the input data, which mimic the defocused images of droplets. The proposed approach has been assessed based on synthetic images and real sphere blur images. The results demonstrate that our method has satisfactory performance both in terms of depth location estimation and droplet size measurement, e.g., the diameter relative error is less than 5% and the location error is less than 1 mm for the sphere with a diameter of more than 1 mm. Moreover, the present model also exhibits considerable generalization and robustness against the transparent ellipsoid and the random background noise. A further application of the present model to the measurement of transparent water droplets generated by an injector is also explored and illustrates the practicability of the present model in real experiments. The present study indicates that the proposed learning-based method is promising for the three-dimensional (3D) measurement of spray droplets via a combination of shadowgraphy techniques using a single camera, which will greatly reduce experimental costs and complexity. Published under an exclusive license by AIP Publishing.

关键词： Drops

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Enhanced image processing for Automobile Accident Detection Using deep learning 3

Enhanced Image Processing for Automobile Accident Detection ...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Usharani, S. Dhanushkumar, J. Manju Bala, P. Balachandar, A. IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Machine Learning Villupuram India IFET College of Engineering An Autonomous Institution Department of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350391565

Road safety can be creatively increased by utilizing systems for reporting and detecting accidents use the YOLO algorithm. Yolo, which stands for "You Only Look Once,"is a sophisticated object recognition system that can identify and pinpoint objects in live video streams. By identifying accidents and notifying emergency services, the system lowers the reaction times and increases the possibility of saving lives by utilizing the YOLO algorithm. The object detection and alarm systems are the two primary parts of the proposed system. The YOLO method is employed by the object recognition module to look for accidents in live video broadcasts. A module of accident photos was used to teach the system to accurately identify incidents. When an accident is discovered, an alert system is activated. The location of the accident and a brief account of what transpired are communicated to the emergency services by the alert system. This information is communicated to emergency services through a wireless communication network, which expedites response times and increases the likelihood of saving lives. Positive results were obtained by testing the system using the images and an accident module. It was shown that the warning system could react in a couple of seconds and that the YOLO algorithm could identify accidents having a precision of around 94. Highways, busy intersections, and other high-risk areas may have systems in place to increase traffic safety and lower the number of accidents. Developing crash detection and warning systems using deep learning using the YOLO technique is one way to potentially increase road safety. With the use of technology, problems may be precisely identified in real-time video feeds, alerting emergency services and potentially increasing survival rates and reaction times. © 2024 IEEE.

关键词： image enhancement

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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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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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Research on rail surface crack identification and detection based on deep learning and image processing 2

Research on rail surface crack identification and detection ...

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2nd International Conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Yu, Wang Rong, Miaobing Min, Daihu Ying, Zhang Guang, Zhaoyi Southwest Jiaotong University State Key Laboratory of Traction Power Chengdu Sichuan610031 China College of Advanced Manufacturing Technology Guangdong Institute of Mechanical and Electrical Technology Guangzhou510550 China

ISBN: (纸本)9798350323818

With the implementation of the development program of our country's transportation country, the traffic construction has welcomed a booming development, the passenger transportation and freight transportation increasing year by year, but it brings more and more high requirements on the quality of the steel rail, the steel rail due to the long-term service and all kinds of defects appear, especially with the rolling contact fatigue cracks. A method and system of rail surface crack identification and detection based on deep learning is proposed. The crack features can be accurately extracted from the complex image of rail surface for real-time crack identification and detection. The crack width can be identified as 0.1mm, and the recognition speed can reach 1-2ms. The rapid identification and detection of rail surface cracks are completed. © 2023 IEEE.

关键词： Freight transportation

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