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Vehicle Registration Plate recognition Using YoloV8 and IOT

Vehicle Registration Plate Recognition Using YoloV8 and IOT

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AIML-Applications for Engineering & Technology (ICAET), international conference on

作者： Juber Nadaf Amol Kadam Pramod Jadhav Prasad Kadam Vinod Patil Milind Gaikwad Department of Computer Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of CSBS Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of E & TC Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of Information Technology Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Number Plate recognition or the vehicle License Plate recognition is an interesting topic of research in smart cities which utilizes image processing techniques. Since the number of cars is rising at an exponential rate, automated systems are supposed to store vehicle data for a variety of applications. In this intended algorithm, an efficient technique has been identified for recognition of different vehicle number registration plates. Through this technique, we can easily deal with problems like low light, irregular font number plates, wrong angles. We use the method of Optical Character recognition to identify characters that are extracted from the registration plate. Even though this project works efficiently for common use cases, it has its own drawbacks and limitations even though we are not using deep learning or any other machine learning algorithms. One of the important steps in vehicle plate recognition is the character recognition which is a technique to obtain individual text characters. YOLOv8 (You Only Look Once), and Darknet- 53 are the methods that is used to extract the features of the data. In this model, the input data is the image of the vehicle that is captured from the camera sensors or cell phones and later cropped to display only the registration plate. We undergo data-pre-processing to improve the quality or the pixels of image on the number plate. Testing is done using two different models one is the model which is obtained with more pre-processing data and the other obtained without any pre-processing data. We evaluate two datasets of number plate images: one with no interference and one where the colour intensity of the image has been decreased to test, the accuracy of the model

关键词： Deep learning YOLO Training image recognition Accuracy Feature extraction Data models Internet of Things Character recognition License plate recognition

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1st international conference on Artificial Intelligence and its Applications, ICAIA 2023

1st International Conference on Artificial Intelligence and ...

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1st international conference on Artificial Intelligence and its Applications, ICAIA 2023

ISBN: (纸本)9783031843938

The proceedings contain 37 papers. The special focus in this conference is on Artificial Intelligence and its Applications. The topics include: The Hybrid Cardiac Risk Assessment and Prediction Model Using Convolutional Neural Networks;deep learning Applications for Malaria Detection and Diagnosis: A Review;Environmental Considerations in the Ethics of AI Adoption in Healthcare: striving for Sustainable and Responsible Practices;harnessing the Power of Cognitive Computing: Assessing Point-of-Care Decision Support Tools in Oncology Practice;Critical Analysis in Use of AI in Health Care Management;classification and Prediction of Spinal Tuberculosis Disease Using Optimization of Convolution Neural Network Using Spatial and Temporal Constraints;artificial Intelligence for Remote Healthcare in Underserved Areas: Enhancing Access and Quality of Healthcare Delivery;machine learning Based Skin Cancer Detection and recognitions Techniques in IoT Environment;validation of a Chronic Kidney Disease Prediction System Using machine learning Techniques;real-Time Feedback Detection Using Emotion Detection and Facial recognition;revolutionizing Vision Tasks: Unlocking Potential Through Patch-Based Approaches;automated Knee Implant Identification from 2D Templates Using image processing and Artificial Intelligence – An Experimental Approach;crop Analysis and Classification Based on Phenotype Using Ensemble learning;Classification and Identification with Health Benefit Assessment and Nutrient Profile of Brewed Tea Utilizing Computer Vision with ML and DL and Sensory Approaches;enhancing Industrial Automation Flexibility Through Neural Network-Empowered machine Vision Applications.

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1st international conference on Artificial Intelligence and its Applications, ICAIA 2023

1st International Conference on Artificial Intelligence and ...

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1st international conference on Artificial Intelligence and its Applications, ICAIA 2023

ISBN: (纸本)9783031843969

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ISAApp – image Based Smart Attendance Application 1st

ISAApp – Image Based Smart Attendance Application

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

作者： Dutta, Aritra Suseela, G. Niranjana, G. Boral, Pushpita Gupta, Pranav Pal, Subha Bal Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9783031730641

This research paper introduces a Python-based implementation of a facial recognition system utilizing face recognition and Open CV libraries. The system has diverse applications, including security, surveillance, social media, and entertainment. By employing a pre-trained neural network, the system enables users to select an image file and accurately detect and recognize faces within it. Additionally, it includes functionality to encode faces from a specified folder, enabling comparison against the faces in the target image. By employing a blend of face detection, feature extraction, and machine learning algorithms, the system effectively identifies, and labels faces in images. Its effectiveness illustrates the potential of facial recognition technology for numerous applications, while also underscoring the ethical and privacy concerns associated with such technology. Addressing these issues and developing more precise and dependable systems would require further research. The primary objective of the system is to offer an improved solution for classroom attendance, ensuring the avoidance of false attendance records and significantly reducing the time required for attendance taking, through the application of machine learning solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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1st international conference on Computational Technologies and Electronics, ICCTE 2023

1st International Conference on Computational Technologies a...

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1st international conference on Computational Technologies and Electronics, ICCTE 2023

ISBN: (纸本)9783031819346

The proceedings contain 27 papers. The special focus in this conference is on Computational Technologies and Electronics. The topics include: Wildlife Detection Using ANN and Other Modern Technology: A Survey of Literatures;enhancing learning Outcomes Through the Use of Conducive learning Spaces;smart Diagnosis Using Symptoms for Seeking a Specialist Doctor;a Comprehensive Review and Future Prospects of Lie Detection Using machine learning;searching Optimizers for Deep learning Based Hyperspectral image Classification;efficient Crop Recommendation System: A machine learning Based Approach;Human Fall Detection Using Transfer learning-Based 3D CNN;anomaly Detection in Respiratory Events Using machine learning;federated learning to Speed Up Pre-processing of Large Data Sets;flood Susceptibility Zonation Using Geospatial Frequency Ratio and Artificial Neural Network Techniques within Himalayan Terai Region: A Comparative Exploration;a Fertilizer Recommendation System Using an Assembly of Regressors Coupled with Nature-Inspired Optimization Algorithms;state-of-the-Art in Feature Selection: Applications of the Slime Mould Algorithm;MOODBYTBLB: Impact of Covid-19 Among Indians: A Sentiment Analysis Using Textblob;mythBuster: A Comparative Analysis of Few machine learning and Deep learning Models for Fake News Detection;unsupervised Approach for Word Sense Disambiguation in Bengali;a Hybrid Method for Bengali Word Segmentation from Handwritten Copies of School students;securing Social Spaces: Harnessing Deep learning to Eradicate Cyberbullying;exploring Intonation patterns in Nepali Speech: A Phonetic and Linguistic Analysis for Text-to-Speech System;sentiment Analysis on Airline Customer Review Using Language Model and Capsule Network;implementation of Digital Healthcare for Improving Maternal Care: A Systematic Review;video Content Analysis and Classification Based on Human Activity recognition;machine learning Based Expert System for Breast Cancer Prediction (MLESBCP).

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Advancing Peer Review Integrity: Automated Reviewer Assignment Techniques with a Focus on Deep learning Applications 1st

Advancing Peer Review Integrity: Automated Reviewer Assignme...

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1st international conference on Computation of Artificial Intelligence and machine learning, ICCAIML 2024

作者： Bhaisare, Bhumika Bharati, Rajesh Department of Computer Engineering Dr. D. Y. Patil Institute of Technology Pune Pimpri India

ISBN: (纸本)9783031714801

The reviewer assignment process in academic research papers is crucial for ensuring peer review integrity and fairness, and it is often referred to as the reviewer assignment problem (RAP). This initial step lays the foundation for the entire review process. To address the limitations of manual reviewer assignment, there is a growing trend among journal and conference editors to explore automated solutions. For quite some time, machine learning techniques have found application in this domain, recent attention has shifted toward the application of Deep learning in the education sector. Deep learning, rooted in neural networks with diverse processing components, is gaining prominence in fields like natural language processing (NLP) and image recognition. In this comprehensive review, we aim to evaluate the key research achievements in the realm of reviewer assignment algorithms. It commences by presenting background information and discussing the necessity for automated reviewer assignment. The paper then systematically examines the existing research in this domain, with a particular focus on recent advancements in Deep learning techniques. The review provides an unbiased assessment of the current algorithms’ strengths and weaknesses. Overall, this analysis emphasizes the growing significance of RAP research and the demand for further innovation and framework development in this field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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1st international conference on Computational Technologies and Electronics, ICCTE 2023

1st International Conference on Computational Technologies a...

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1st international conference on Computational Technologies and Electronics, ICCTE 2023

ISBN: (纸本)9783031819803

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Automated Breast Cancer Detection: A Review 4th

Automated Breast Cancer Detection: A Review

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

作者： AlKhater, Rozah Al-maadeed, Somaya Qatar University Doha Qatar

ISBN: (纸本)9783031821554

Over the past decade, medical imaging research has grown significantly in image processing and computer vision, particularly in detecting, classifying, and segmenting breast cancer. Advancements in pattern recognition and Deep learning (DL) methodologies have significantly impacted this field. In light of the swift advancements in deep learning technology and the escalating seriousness of breast cancer, it is imperative to synthesize previous achievements and pinpoint forthcoming obstacles that require attention. Thus, this paper extensively reviews the classification of histopathological images for breast cancer detection using machine learning and deep learning techniques. The article emphasizes the publicly accessible datasets of histopathological images for classifying breast cancer. This article examines and outlines recent research on histopathology imaging for breast cancer screening and explores potential future developments. Upon reviewing the literature, it was discovered that only a limited number of studies utilize image processing and machine learning approaches. Most research in the past five years has utilized deep learning techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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1st international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2016

1st International Conference on Recent Trends in Image Proce...

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1st international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2016

ISBN: (纸本)9789811048586

The proceedings contain 39 papers. The special focus in this conference is on Recent Trends in image processing and pattern recognition. The topics include: Word-level thirteen official Indic languages database for script identification in multi-script documents;skew detection and correction of devanagari script using interval halving method;Eigen value based features for offline handwritten signature verification using neural network approach;an approach for logo detection and retrieval in documents;spotting symbol over graphical documents via sparsity in visual vocabulary;word retrieval from Kannada document images using hog and morphological features;hand drawn symbol recognition in immersive virtual reality using deep extreme learning machines;comparative study of handwritten Marathi characters recognition based on KNN and SVM classifier;recognition of handwritten devanagari numerals by graph representation and lipschitz embedding;combination of feature selection methods for the effective classification of microarray gene expression data;pattern recognition based on hierarchical description of decision rules using choquet integral;classification of summarized sensor data using sampling and clustering: a performance analysis;feature reduced weighted fuzzy binarization for histogram comparison of promoter sequences;a fast k-nearest neighbor classifier using unsupervised clustering;DSS for prognostication of academic intervention by applying DM technique;cluster based symbolic representation for skewed text categorization;performance analysis of impulse noise attenuation techniques;industrial applications of colour texture classification based on anisotropic diffusion and a comprehensive method for assessing the blepharospasm cases severity.

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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