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image processing Technique for Tracking and Counting of Vehicles Using ROI

Image Processing Technique for Tracking and Counting of Vehi...

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2023 International conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kumar, Ankit Chandel, Ritika Singh, Monika Apex Institute of Technology Chandigarh University Mohali India

ISBN: (纸本)9798350357769

This research paper presents a novel approach for vehicle tracking and counting utilizing the advanced object detection model YOLOv8 in the field of image processing. The accurate monitoring of vehicular traffic is crucial for various applications, including traffic management, urban planning, and safety analysis. Existing methods often struggle with real-time tracking and counting of vehicles due to challenges such as occlusions, varying lighting conditions, and complex traffic scenarios. In our proposed methodology, we leverage the power of YOLOv8, a deep learning-based object detection model, uses real-time detection to identify and monitor vehicles, ensuring high accuracy and robustness in various traffic environments. The proposed approach provides a valuable tool for traffic management authorities and urban planners, offering precise data for traffic flow analysis and infrastructure development. © 2023 IEEE.

关键词： Deep Learning image processing machine vision Object Detection ROI (Region of Interest) Vehicle Detection YOLO (You Only Look Once)

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2023 5th International conference on Artificial Intelligence and Computer applications, ICAICA 2023

2023 5th International Conference on Artificial Intelligence...

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2023 5th International conference on Artificial Intelligence and Computer applications, ICAICA 2023

ISBN: (纸本)9798350323313

The proceedings contain 173 papers. The topics discussed include: restricted area sign detector using YOLO v5;research on distance teaching course interactive system based on computer algorithm research data;APT detection and attack scenario reconstruction based on big data analysis;new image processing: VGG image style transfer with gram matrix style features;trajectory measurement and positioning of underwater vehicle based on monocular stereo vision;the importance of multi feature extraction and fusion for prediction of protein subcellular localization;design and implementation of FPGA-based four-dimensional ultra chaotic system;flocking towards a robust mobile network topology;real time speech recognition method for online complaints from power grid customers based on improved residual network;optimization of parking space detection system based on ZigBee wireless sensor network;and a wire drawing defect detection approach for FDM 3D printing based on machine vision technology.

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LoG-VMamba: Local-Global vision Mamba for Medical image Segmentation 17th

LoG-VMamba: Local-Global Vision Mamba for Medical Image Seg...

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17th Asian conference on Computer vision, ACCV 2024

作者： Dang, Trung DQ. Nguyen, Huy Hoang Tiulpin, Aleksei University of Oulu Oulu Finland

ISBN: (纸本)9789819609000

Mamba, a State Space Model (SSM), has recently shown competitive performance to Convolutional Neural Networks (CNNs) and Transformers in Natural Language processing and general sequence modeling. Various attempts have been made to adapt Mamba to Computer vision tasks, including medical image segmentation (MIS). vision Mamba (VM)-based networks are particularly attractive due to their ability to achieve global receptive fields, similar to vision Transformers, while also maintaining linear complexity in the number of tokens. However, the existing VM models still struggle to maintain both spatially local and global dependencies of tokens in high dimensional arrays due to their sequential nature. Employing multiple and/or complicated scanning strategies is computationally costly, which hinders applications of SSMs to high-dimensional 2D and 3D images that are common in MIS problems. In this work, we propose Local-Global vision Mamba, LoG-VMamba, that explicitly enforces spatially adjacent tokens to remain nearby on the channel axis, and retains the global context in a compressed form. Our method allows the SSMs to access the local and global contexts even before reaching the last token while requiring only a simple scanning strategy. Our segmentation models are computationally efficient and substantially outperform both CNN and Transformers-based baselines on a diverse set of 2D and 3D MIS tasks. The implementation of LoG-VMamba is available at https://***/Oulu-IMEDS/LoG-VMamba. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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An Intelligent Self-Driving Car’s Design and Development, Including Lane Detection Using ROS and machine vision Algorithms 1

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3rd International conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Sujatha, E. Sundar, J. Sathiya Jeba Raju, D. Naveen Lakshminarayanan, S. Suganthi, N. Thandalam Chennai India Department of Computer Science and Engineering Velammal Institute of Technology Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Chennai India Sri Sairam Engineering College Chennai India SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

It is challenging to find a solution for lane detection. It has aroused the curiosity of the computer vision field for many years. It has been found that computer vision and machine learning algorithms struggle to tackle the multi-feature identification problem known as lane detection. Even though there are a few different machine learning approaches that may be used for lane identification, these approaches are often employed for classification rather than feature development. On the other hand, contemporary techniques of machine learning may be used to discover features that have a high recognition value, and they have shown success in feature identification tests. These strategies haven’t been applied correctly, which compromises their efficiency and accuracy when it comes to lane recognition. In this study, we provide a fresh approach to solving the problem. A brand-new preprocessing and Region of Interest (ROI) selection method is presented in this article. The major objective is to extract white features by making use of the HSV color transformation, adding preliminary edge feature detection while doing preprocessing, and then selecting ROI based on the preprocessing that was proposed. With the help of this cutting-edge preprocessing strategy, the lane may be found. The integrated autonomous vehicle that we envision is one that is controlled by a Robotic Operating System and that is capable of making intelligent driving choices. The unique filtering and noise reduction techniques that were used on the visual feedback by means of the processing unit served as the basis for the digital image-processing algorithm that was responsible for the greatest performance achieved by the autonomous vehicle. Within the control system, we used two separate control units, one of which was a master and the other of which was a slave. The master control unit is in charge of the visual processing and filtering, while the slave control unit is in charge of the vehicle’s propulsio

关键词： Autonomous vehicles

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Padding-Free Convolution Based on Preservation of Differential Characteristics of Kernels 22

Padding-Free Convolution Based on Preservation of Differenti...

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22nd IEEE International conference on machine Learning and applications, ICMLA 2023

作者： Leng, Kuangdai Thiyagalingam, Jeyan Science and Technology Facilities Council Scientific Computing Department Didcot United Kingdom

ISBN: (纸本)9798350345346

Convolution is a fundamental operation in image processing and machine learning. Aimed primarily at maintaining image size, padding is a key ingredient of convolution, which, however, can introduce undesirable boundary effects. We present a non-padding-based method for size-keeping convolution based on the preservation of differential characteristics of kernels. The main idea is to make convolution over an incomplete sliding window 'collapse' to a linear differential operator evaluated locally at its central pixel, which no longer requires information from the neighbouring missing pixels. While the underlying theory is rigorous, our final formula turns out to be simple: the convolution over an incomplete window is achieved by convolving its nearest complete window with a transformed kernel. This formula is computationally lightweight, involving neither interpolation or extrapolation nor restrictions on image and kernel sizes. Our method favours data with smooth boundaries, such as high-resolution images and fields from physics. Our experiments include: i) filtering analytical and non-analytical fields from computational physics and, ii) training convolutional neural networks (CNNs) for the tasks of image classification, semantic segmentation and super-resolution reconstruction. In all these experiments, our method has exhibited visible superiority over the compared ones. © 2023 IEEE.

关键词： computer vision convolutional neural network differential operator machine learning padding

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Heart Signal processing Using Wavelet Analysis and Deep Learning Algorithms Based on Artificial Intelligence

Heart Signal Processing Using Wavelet Analysis and Deep Lear...

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International conference on applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Zilaie, Morteza Mohammadkhani, Zohreh Asrari, Keyvan Azimi Talebiyan, Seyed Reza Islamic Azad University Science and Research Branch Khorasan Razavi Mashhad Iran The University of Pavia Pavia Italy Imam Reza International University Khorasan Razavi Mashhad Iran

ISBN: (纸本)9783031840999

The electrocardiogram signal of the heart is used to monitor the health status and function of the human heart and to a doctor in diagnosing the type of disease. For this purpose, first, the scalogram of the different heart failures produces different types of this signal. In this research, the cardiac signals of the patients are analyzed by an artificial intelligence neural network, and the type of the person’s disease is diagnosed. The artificial intelligence in question can be learned and can be used as an assistant ECG signal is calculated using continuous wavelet transform and then it is subjected to learning and evaluation using a deep convolutional neural network. In the architecture of artificial intelligence, two well-known neural networks GoogLeNet and SqueezeNet are used, which have been sufficiently trained in similar applications such as image processing and machine vision. In the end, the type of heart failure is diagnosed and classified. The cardiac signals of the patients used in the simulation were extracted from the PhysioNet medical engineering standard dataset of MIT University. In the end, the simulation results of the research are given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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applications of Diffusion Model image Restoration in the Field of Heritage Restoration: Overview and Outlook 3

Applications of Diffusion Model Image Restoration in the Fie...

引用

3rd International conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Li, Yiran Nanjing University of Finance and Economics Qixia District Jiangsu Province Nanjing China

ISBN: (纸本)9781643684444

The restoration of museum heritage is an important task with significant cultural and historical value;however, traditional methods of restoration are frequently constrained by the extent of the damage to the heritage as well as the constraints of the restoration techniques. In recent years, a method of restoration known as the diffusion model, which is based on computer vision and machine learning, has been gradually applied to the field of museum relics restoration and has shown enormous potential in relic restoration. This method of restoration was developed in the 1980s and is still in use today. In the field of image restoration, research and application of diffusion models are reviewed, and this article provides a summary of the development history, methodology principles, and application cases associated with these topics. This article provides an introduction to the fundamental ideas and principles underlying diffuse models, as well as a summary of the current state of research and an outlook on potential future trends and prospects pertaining to image repair within the domain of image processing. © 2023 The Authors.

关键词： image reconstruction

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image Labeling Using Convolutional Neural Network

Image Labeling Using Convolutional Neural Network

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2023 IEEE International conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Vasantha Kumari, N. Garimella, Mounika Bhan, Madhu Ramaiah Institute of Technology Dept. of Mca Bengaluru India Ramaiah Institute of Technology Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350300826

Due to the rapid rise in the identification of digital materials, automatic image classification has emerged as the most difficult topic of computer vision. In comparison to human vision, automatic visual understanding and analysis by a system is challenging. The findings of many experiments that were done to fix problems of automatic picture classification with the current categorization method were simple picture primitives. However, those methods don't accurately classify photos. In this research work, a system has been created that makes use of deep learning to deliver the necessary results in the area of computer vision. Our technology automatically categorizes photographs using Convolutional Neural Network, a popular machine learning method. Our algorithm classifies grayscale photos using the Digit of MNIST dataset as a benchmark. The dataset that is employed for the training of the system contains grayscale images, which demand higher processing resources for image categorization. By employing Convolution Neural Network to train the photos, we were able to attain higher accuracy results in the experimental section, demonstrating that our model is highly accurate at classifying images. © 2023 IEEE.

关键词： image classification

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Drone-Assisted Infrared Thermography and machine Learning for Enhanced Photovoltaic Defect Detection: A Comparative Study of vision Transformers and YOLOv8 44th

Drone-Assisted Infrared Thermography and Machine Learning f...

引用

44th SGAI International conference on Innovative Techniques and applications of Artificial Intelligence, SGAI 2024

作者： Memari, Ammar Debich, Tarek Jade University of Applied Sciences Friedrich-Paffrath-Straße 101 Wilhelmshaven26389 Germany

ISBN: (纸本)9783031779176

This paper presents a comparative study on the application of drone-assisted infrared thermography coupled with state-of-the-art machine learning models, including vision Transformers (ViTs) and YOLOv8, for efficient and accurate defect detection in Photovoltaic (PV) systems. The research outlines the methodology for on-site inspections and details the integration of drones to capture high-resolution thermal imagery, identifying minute anomalies indicative of potential system failures. By employing advanced image processing techniques and training AI models, the study compares the performance of these models in accurately identifying and classifying PV defects. A segmentation model based on TensorFlow was trained and used to detect the location of PV panels in the camera imagery, followed by the separate application of YOLOv8 and ViTs to classify defects in the detected PV panels. The dataset used to train the models was carefully curated and prepared specifically for this study, representing another contribution of this paper. This approach not only enhances detection capabilities but also streamlines the processes of data collection, analysis, and reporting, ultimately leading to improved decision-making for system operators and stakeholders. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Thermography (imaging)

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OPTIMIZING CNN PERFORMANCE ON CIFAR-10: A STUDY ON MULTI-CORE processing, BATCHING TECHNIQUES, AND FUTURE NETWORK ARCHITECTURES 2

OPTIMIZING CNN PERFORMANCE ON CIFAR-10: A STUDY ON MULTI-COR...

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2nd International conference on Mechatronic Automation and Electrical Engineering, ICMAEE 2024

作者： Jin, Datong College of Art and Science The Ohio State University Columbus43210 United States

ISBN: (纸本)9781837242672

The performance of Convolutional Neural Networks (CNNs) is critically dependent on the underlying computational configurations, particularly in terms of processing capabilities and data handling techniques. As deep learning models become increasingly complex and data-intensive, the choice between single-core and multi-core processing, along with strategies such as batching, becomes pivotal. These configurations directly impact the efficiency and speed of model training and inference, which are essential for applications requiring real-time processing and high accuracy, such as image recognition and automated systems. Study Contributions: This research focuses on comparing the effects of single-core versus multi-core processing configurations on CNN performance, integrating batching techniques to observe their combined influence on reducing latency and increasing throughput. The study reveals that leveraging multi-core processing with batching substantially enhances CNN operations. Additionally, it addresses the deployment challenges of the YOLOv5 architecture, emphasizing the necessity for further architectural improvements. The paper suggests that future enhancements could include adopting deeper network structures to elaborate on complex features and employing methods like ResNet to counteract gradient vanishing problems. Moreover, it advocates for the fine-tuning of models with pre-trained datasets such as imageNet to boost robustness and accuracy, particularly demonstrated on the CIFAR-10 dataset. These recommendations aim to refine CNN capabilities, thereby advancing the broader field of machine learning and computer vision. © The Institution of Engineering & Technology 2024.

关键词： Convolutional neural networks

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