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4th International Conference on Advances in Electrical Engineering and Computer applications, AEECA 2023

作者： Li, Changgeng Xu, Feng Li, Wei Sun, Xiaodong Yuan, Bo Crrc Qingdao Sifang Co. Ltd Qingdao266111 China

ISBN: (纸本)9798350308082

In response to the demand for efficient and high-quality daily maintenance and inspection of modern railway trains, the utilization of AI-based machine vision technology has been proven to significantly enhance maintenance efficiency and quality. image quality plays a critical role in visual inspection. Thus, this study presents the design of a dynamic train exterior image acquisition system based on line scan camera technology. Through a comprehensive analysis of maintenance requirements and business scenarios, the hardware structure and functional framework of the system are meticulously developed. The image acquisition module is strategically deployed in a 360° arrangement to enable panoramic image acquisition of the train's exterior. Leveraging the imaging principle of the line scan camera, two sets of magnetic cylinders are employed to measure the train's velocity multiple times, ensuring continuous correction of the image acquisition line frequency for stable image capture. Furthermore, a character recognition algorithm for train numbers is extensively researched, enabling accurate identification of the train number through image acquisition, preprocessing, and OCR recognition algorithms. The proposed system integrates professional image acquisition and processing techniques, enabling high-resolution and high-precision stable acquisition of dynamic train images. It provides crucial data support for precise detection of train exterior conditions and offers reliable technical means for train trajectory monitoring and safety management. © 2023 IEEE.

关键词： image acquisition

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International Conference on VLSI, Signal processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

International Conference on VLSI, Signal Processing, Power E...

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International Conference on VLSI, Signal processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

ISBN: (纸本)9789819746569

The proceedings contain 35 papers. The special focus in this conference is on VLSI, Signal processing, Power Electronics, IoT, Communication and Embedded Systems. The topics include: Sensor Perception Reliability in Automated Vehicles;design and Implementation of 16-Bit Posit Arithmetic;emergence, Evolution, and applications of Medical Cyber-Physical Systems;smart Insole Design for Foot Pressure Monitoring;an Efficient Cryptographic Technique to Improve Data Privacy and Security in E-Commerce;an Iot-Based Transmission Line Fault Monitoring System;performance Analysis of the System of IoT Architecture;real-Time Implementation of IoT-Based Home Automation System on Arduino Platform;Data Security Paradigms: Paillier Homomorphic Encryption and AES in the Context of Privacy-Preserving Computation;a Review on Watermarking Techniques of Digital image;An Improved Pre-charging Strategy for Asymmetrical Mixed-Cell Submodule (SM) Based Modular Multilevel Converter;classification of Remotely Sensed Data Using Fisher’s Linear Discriminant;design of Smart Wheelchair for Disabilities with Quadriplegia Through Brain–Computer Interface;Automatic Load Distribution of Power During Low Power Generation Using PLC;AI-driven Lung Cancer Detection for Rapid Analysis of Medical Imaging Data;comparative Study of Transfer Learning Models for Mushroom Classification;automated Software Defect Prediction Model: AdaBoost-Based Support Vector machine Approach;Preprocessing of MRI Data for the Early Detection of Alzheimer’s Disease Using DWT;classification of Soybean Seed Using Support Vector machine with image Enhancement Techniques;From Deployment to Drift: A Comprehensive Approach to ML Model Monitoring with Evidently AI;a Real-Time Computer vision-Based Green Intensity Estimation Method for the Arecanut Tree Pesticide Spraying System;key Enabling Technologies for Next-Generation Wireless Systems.

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A systematic literature review of defect detection in railways using machine vision-based inspection methods

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International Journal of Transportation Science and Technology 2024年

作者： Kumar, Ankit Harsha, S.P. Advanced Mechanical Vibration Laboratory Mechanical and Industrial Engineering Department IIT Roorkee India IIT Roorkee India

Train rolling stock and track inspections are necessary for the safe operation of the train. For this reason, a regular inspection of defects is required for the train rolling stock. The conventional defect detection methods yield low efficiency, consume more time, are unreliable, and are less cost-effective. These obstacles may be mitigated by integrating a machine vision-based inspection system (MVIS). This systematic literature review explores the landscape of railway defect detection methodologies, primarily focusing on leveraging image processing techniques. This comprehensive analysis encompasses many studies examining the evolution of image processing applications in the context of railway rolling stock and rail track defect detection. From traditional methods to the latest advancements, a nuanced understanding of the challenges and innovations in this domain is required. Key themes include utilizing computer vision algorithms, machine learning models, and deep learning techniques for enhanced accuracy in identifying defects. We delve into the intricacies of image acquisition, preprocessing, and feature extraction, shedding light on the pivotal role of these processes in refining defect detection systems. Also, the current gaps and opportunities for future research, emphasizing the need for standardized datasets, benchmarking methodologies, and the integration of emerging technologies, are highlighted. This review not only consolidates the existing knowledge but also serves as a roadmap for researchers invested in advancing the field of railway defect detection. By synthesizing insights from many studies, this review contributes to a deeper understanding of the state-of-the-art in railway defect detection using image processing, fostering dialogue and collaboration for improving railway safety and reliability. © 2024 Tongji University and Tongji University Press

关键词： Computer vision

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Contribution Unveiling Cutting-Edge machine Learning Techniques for image Segmentation

Contribution Unveiling Cutting-Edge Machine Learning Techniq...

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International Conference on Electrical and Electronics Engineering, ICEEE 2023

作者： Shaik, Nazeer Gupta, Ankur Bhati, Sunita Kumar, Jaideep Singh, Jagendra Budhiraja, Ishan Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology Andhra Pradesh Anantapur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Applications JECRC University Jaipur India Raj Kumar Goel Institute of Technology AKTU University Lucknow India

ISBN: (纸本)9789819986606

Segregation of images is a critical step in processing images, computer vision, and a variety of other disciplines. The technique involves decomposing an illustration into numerous components or components, every single one that consists of an ensemble of elements with identical features for example African descent, frequency, or consistency. The most important objective of appearance, the intention of fragmentation seems to reduce complexity or customize the mathematical representation of an illustration in a way that is more readily reasonable and more straightforward for assessment. It is widely employed to locate boundaries and features in photos, and this is favorable in an assortment of industries including clinical imaging, object detection, recognition, and autonomous vehicles. Several image segmentation techniques are available, and in incorporating thresholding, zone is an area-based differentiation and corner-based recognition. A threshold segment is a simple and commonly used technique that involves setting a threshold value and dividing the pixels into two classes based on their intensity values. Region-based segmentation involves grouping pixels based on their spatial proximity and similarity in characteristics, while edge-based segmentation involves detecting edges or boundaries in an image and using them to separate different regions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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A review of digital image processing techniques for optical tweezers: from algorithms to applications

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JOURNAL OF MODERN OPTICS 2025年

作者： Tang, Weijie Zhou, Ruohan Hu, Sheng Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Hebei Key Lab Micronano Precis Opt Sensing & Measu Qinhuangdao 066004 Peoples R China

As a ubiquitous manipulation tool, optical tweezers are widely used in biochemistry and applied physics, so that a wide range of microscopic and nanoscopic particles could be investigated. In recent years, digital image processing techniques for improving target particle observation have diversified, leading to the development of numerous automatic tasks. The technique was developed in response to the need for multi-particle manipulation and feature detection. Here we describe how digital image processing can be used to enhance the capabilities of optical manipulation. In particular, cutting-edge image processing techniques that rely on artificial intelligence development are making optical trapping more widely accessible and enabling automatic manipulation of microscopic and nanoscopic particles.

关键词： Optical tweezers image processing machine vision image algorithm artificial intelligence

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A Real-Time Waste Segregation Using machine vision for Recycling Facilities 7

A Real-Time Waste Segregation Using Machine Vision for Recyc...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Qaisar, Muhammad Waqas Fang, Jingyang Masood, Umar School of Control Science and Engineering Shandong University Jinan China School of Electrical Engineering Namal University Mianwali Pakistan

ISBN: (纸本)9798350359558

Recycling facilities are often challenged by the demanding task of waste segregation, a process traditionally requiring substantial time and manpower, and lacking efficient automated solutions. Addressing this, our study explores the use of convolutional neural networks (CNNs), particularly the ResNet-18 model, known for its balance of accuracy and computational efficiency compared to its deeper counterparts like ResNet-50 or ResNet-101. ResNet-18 provides sufficient complexity for precise image classification while enabling faster processing speeds essential for real-time applications. Our method employs this CNN to effectively categorize waste into five distinct types, streamlining the sorting process. In this paper, we introduce a custom-designed hardware system, incorporating motor control for the physical segregation of waste. This system operates in tandem with the ResNet-18's classification results, ensuring that each type of waste is accurately sorted into designated bins. Our integrated approach, which pairs the image classification capabilities of ResNet-18 with a practical hardware automation system, aims to improve the efficiency and accuracy of waste segregation in recycling facilities. © 2024 IEEE.

关键词： Convolution

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A Survey on Quaternion Algebra and Geometric Algebra applications in Engineering and Computer Science 1995-2020

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IEEE ACCESS 2021年 9卷 104326-104355页

作者： Bayro-Corrochano, Eduardo CINVESTAV Guadalajara Dept Elect Engn & Comp Sci Guadalajara Campus Zapopan 45119 Jalisco Mexico

Geometric Algebra (GA) has proven to be an advanced language for mathematics, physics, computer science, and engineering. This review presents a comprehensive study of works on Quaternion Algebra and GA applications in computer science and engineering from 1995 to 2020. After a brief introduction of GA, the applications of GA are reviewed across many fields. We discuss the characteristics of the applications of GA to various problems of computer science and engineering. In addition, the challenges and prospects of various applications proposed by many researchers are analyzed. We analyze the developments using GA in image processing, computer vision, neurocomputing, quantum computing, robot modeling, control, and tracking, as well as improvement of computer hardware performance. We believe that up to now GA has proven to be a powerful geometric language for a variety of applications. Furthermore, there is evidence that this is the appropriate geometric language to tackle a variety of existing problems and that consequently, step-by-step GA-based algorithms should continue to be further developed. We also believe that this extensive review will guide and encourage researchers to continue the advancement of geometric computing for intelligent machines.

关键词： Algebra Quaternions Rotors Three-dimensional displays Robots Computer science Calculus Geometric algebra Clifford algebra quaternion algebra screw theory signal processing electrical engineering and power systems geometric and quantum computing image processing computer vision graphic engineering artificial intelligence machine learning neural networks control engineering robotics biomedical engineering and biotechnology

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A Parallel Sea-Land Segmentation Method Based on Improved Unet Model 3

A Parallel Sea-Land Segmentation Method Based on Improved Un...

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3rd International Conference on Computer vision and Pattern Analysis, ICCPA 2023

作者： Zhang, Xuebo Jiang, Min Li, Jinli School of Space Information Space Engineering University Beijing China 96944 troops Chinese People's Liberation Army Beijing China

ISBN: (纸本)9781510667563

Pixel-level sea-land segmentation on high-resolution remote sensing images is a basic task in remote sensing applications and is of great significance for coastline extraction and near-shore marine target detection. This paper proposes an improved Unet model for sea-land segmentation on high-resolution remote sensing images. This model outperforms UNet++ and other models in sea-land segmentation accuracy when applied to the HRSC2016-SL dataset. Based on this model, a parallel sea-land segmentation processing algorithm was developed, whose parallel efficiency reached 49.5% on 16K*14K remote sensing images. To complete this study, a parallel processing system for sea-land segmentation was developed, which achieved distributed and single-machine multi-core parallel sea-land segmentation tasks on high-resolution remote sensing images. © 2023 SPIE.

关键词： image segmentation

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Comprehensive Weather Recognition Using ResNet-50 and a Novel Weather image Dataset 4

Comprehensive Weather Recognition Using ResNet-50 and a Nove...

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4th International Signal processing, Communications and Engineering Management Conference, ISPCEM 2024

作者： Wang, Jun Zhou, Yunhan Fu, Min School of Electrical and Electronics Engineering UESTC Chengdu China Cyrinn Science & Technology Ltd. Guangzhou China

ISBN: (纸本)9798331528676

Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of AI technology, scientific researchers have trained many models which receive weather images as inputs and output the weather in these images fast and accurately. However, many existing weather recognition models are limited in scope, typically identifying only four types of weather with varying degrees of accuracy. In this study, the two key contributions of this research are: first, the creation of a custom dataset designed specifically for recognizing seven distinct weather conditions, and second, the exploration of multiple machine learning techniques to identify the most effective model for this task. By developing our own dataset, we ensure a broader representation of weather types, allowing the models to train and be tested on more diverse and realistic scenarios. We evaluate five different algorithms: Support Vector machine (SVM), Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and ResNet50. To enhance model generalization and reduce overfitting, we employ data augmentation techniques and add dropout layers in the CNN and ResNet50 architectures. Through extensive experimentation, ResNet50 emerges as the best-performing model, achieving an accuracy of approximately 87% on the custom dataset. This work demonstrates the importance of creating domain-specific datasets and using a range of machine learning methods to develop accurate and reliable weather recognition models. Our approach offers a robust solution for outdoor vision systems, with potential applications in autonomous vehicles, surveillance systems, and other environments where accurate weather detection is critical for safety and performance. ©2024 IEEE.

关键词： Convolutional neural networks

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Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review

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MULTIMEDIA TOOLS AND applications 2021年第13期80卷 19931-19946页

作者： Bhargava, Anuja Bansal, Atul GLA Univ Mathura India

The universal transmission of pandemic COVID-19 (Coronavirus) causes an immediate need to commit in the fight across the whole human population. The emergencies for human health care are limited for this abrupt outbreak and abandoned environment. In this situation, inventive automation like computer vision (machine learning, deep learning, artificial intelligence), medical imaging (computed tomography, X-Ray) has developed an encouraging solution against COVID-19. In recent months, different techniques using image processing are done by various researchers. In this paper, a major review on image acquisition, segmentation, diagnosis, avoidance, and management are presented. An analytical comparison of the various proposed algorithm by researchers for coronavirus has been carried out. Also, challenges and motivation for research in the future to deal with coronavirus are indicated. The clinical impact and use of computer vision and deep learning were discussed and we hope that dermatologists may have better understanding of these areas from the study.

关键词： Computer vision Computed tomography machine learning Coronavirus COVID-19

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