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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Patil, R.S. Deshpande, Sakshi Khan, Hassan Mhatre, Prajakta Bharati Vidyapeeth College of Engineering Department of Electronics and Telecommunication Engineering Maharashtra Mumbai India

ISBN: (纸本)9798350348002

As each vehicle is uniquely acknowledged by its license plate, the Transport System places a high priority on finding and recognizing of license plates. The news is constantly reporting on accidents and missing cars. Authorities must acknowledge all of these unlawful acts. As a result, research into the identification and recognition of vehicle number plates is ongoing. However, identifying a vehicle's number plate has always been difficult for a number of reasons, such as brightness changes, shadows cast by moving vehicles, erratic license plate character types, different plate styles, and color effects caused by the surroundings. In this system, Number plate of vehicle is detected from a live video or an image. There is image preprocessing and segmentation done on the live video or number plate image. deep learning model methods are used, the characters from it are separated and then each character gets recognized. This helps to collect the vehicle overall project then the capabilities of different techniques into one integrated automatic system are summarized. This kind of systems can be implemented on the roadside and makes a real time comparison between passing car and list of stolen cars. This detected license plate number could also be used in car parking systems. PUC which stands for Pollution Under Control, where emission levels of vehicles and the regular renovation of the PUC certificate is done or not is verified and the details are shown. This will help in keeping an overall check on vehicles and the task which most of the places do manually to check the PUC certificate for checking status, can be verified quickly and the fine can be implemented as per so. © 2023 IEEE.

关键词： Optical character recognition

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Experimental Analysis of Malarial Parasites Infection by using deep learning based on Medical image processing Logic

Experimental Analysis of Malarial Parasites Infection by usi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： N Meenakshisundaram Sajiv G Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science (SIMATS) Saveetha University Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Malaria remains a significant global health challenge, particularly in resource-limited regions, necessitating accurate and rapid diagnostic tools. This study introduces deepMalariaNet, a deep learning model developed for detecting and classifying malarial parasites using the “Malaria Parasite image - Malaria Species” dataset from Kaggle. The model employs Residual Attention Mechanisms and Parallel Convolutional Stacks (PCS) to improve diagnostic accuracy by focusing on critical image regions and capturing multi-scale features. Experimental results demonstrate that deepMalariaNet achieves 98.5% accuracy for binary classification (infected vs. non-infected) and 95.2% for multiclass classification (species identification). The model's robustness is validated through 10-fold cross-validation and ablation studies, and it outperforms state-of-the-art models such as ResNet-50 and DenseNet-121 in both accuracy and inference time. deepMalariaNet shows significant promise for real-time malaria detection in clinical settings, contributing to early and accurate diagnosis, which is crucial for effective malaria control and treatment.

关键词： deep learning Accuracy Attention mechanisms Malaria Parasitic diseases Sensitivity and specificity Metadata Robustness real-time systems Integrated circuit modeling

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deep learning with image processing for Object Detection in Traffic Cameras 8

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

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8th International Conference on Trends in Electronics and Informatics, ICOEI 2025

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

ISBN: (纸本)9798331544607

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. © 2025 IEEE.

关键词： Convolutional neural networks

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Lane detection using the concept of deep learning and Digital image processing

Lane detection using the concept of Deep learning and Digita...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Shashi Kant Ojha Abhishek Kumar Sandeep Kumar CSE Department Sharda University Greater Noida

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Lane detection technology plays a pivotal role in enabling autonomous navigation in vehicles. However, existing systems primarily cater to well-structured roads with clear lane markings, rendering them ineffective in scenarios where markings are unclear or absent. This study critically evaluates an existing approach for detecting lanes on unmarked roads, followed by the proposal of an enhanced methodology. Both approaches leverage digital image processing techniques and rely solely on vision or camera data. The primary objective is to derive real-time curvature values to facilitate driver-assistance systems in making necessary turns and preventing vehicles from veering off-road.

关键词： deep learning Computers Lane detection Navigation Roads Digital images Rendering (computer graphics) Cameras real-time systems Proposals

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Enhancing Palm Recognition Accuracy Through image processing deep learning Techniques

Enhancing Palm Recognition Accuracy Through Image Processing...

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Data Science and Network Security (ICDSNS), International Conference on

作者： J. Sheela Mercy S. Silvia Priscila Department of Computer Applications Bharath Institute of Higher Education and Research Chennai India Department of Computer Science Bharath Institute of Higher Education and Research Chennai India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Palm recognition systems play an important role in biometric authentication; however, existing systems frequently have low accuracy and resiliency due to problems such as changing lighting conditions, occlusions, and hand orientations. The paper describes a novel way to improve palm recognition accuracy by combining modern image processing and deep learning approaches. Unlike traditional systems that utilize handcrafted features and shallow learning algorithms, the proposed system uses convolutional neural networks (CNNs) for feature extraction and classification, as well as advanced image preprocessing techniques. The technology preprocesses palm images to increase quality and consistency, thereby reducing the impact of environmental factors. Cross-validation trials yielded consistent results, with an average accuracy of 95%, precision of 92%, recall of 96%, and Fl-score of 94%. Furthermore, computational efficiency comparisons reveal that the proposed system beats existing ones in terms of training time, inference time, and model size, indicating that it is a promising option for accurate and efficient palm recognition in a variety of real-world applications.

关键词： Training deep learning Accuracy image recognition Computational modeling Lighting Biometric authentication

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Determination of License Plate Using deep learning and image processing 6th

Determination of License Plate Using Deep Learning and Image...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Tyagi, S.S. Rathore, Riti Rathore, Vaibhav Rai, Shruti Rohila, Shruti Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9789819933143

Automatic license plate recognition (ALPR) provides a way to extract the vehicle’s number plates using the computer vision technology. To achieve higher accuracy rates, ALPR is widely used across the world in traffic controlling, automated toll collected system and in society parking, etc. Any ALPR system consists of following processes, viz. video recording as an input, identifying that object is moving or not, differentiating between vehicles and non-vehicles, localization of license plate of vehicles, extractions of characters of license plate, and recognition of segmented characters. In the real world, due to changing weather conditions, polluted environments, different intensities of headlight, etc., it is very difficult to capture an input image. The ALPR processes become more difficult if the license plates of each vehicle do not follow the rules and protocols set by the corresponding Motor Vehicles Department. If the license plate is broken, tilted, dirty, or missing some characters, then all these difficulties add up to generate an efficient ALPR system. Traditional LPR methods extract characters by one-by-one characters segmentation which is very time consuming, but our method provides image preprocessing, machine learning, and neural network techniques which makes it time saving and achieves greater accuracy rates. Neural network technique consists of image preprocessing, contour extraction, rotation, distortion, and character segmentation processes. Experimentally, we can verify our proposed method on a public dataset, and the results state that our method overcomes the drawbacks of previous methods and it achieves accuracies of more than 97% for different types of license plate datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Video recording

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TrashIntelAI- Garbage Collection Automation using deep learning and image processing

TrashIntelAI- Garbage Collection Automation using Deep Learn...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Ranjana Jadhav Aditya Mhaske Aditya Nawale Mokshit Oswal Sumit Mulhar Dept. of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India

Garbage collection in urban areas has become a major challenge due to the increase in trash production. New technologies, including the application of deep learning and image processing methods, have been created to solve this problem. In this study, we present an automated garbage collection system that recognizes and categorizes rubbish in real-time using computer vision and machine learning methods. A deep learning model classifies the garbage into several categories, and the system also contains an image processing pipeline that pre-processes incoming photos and extracts relevant data. We executed experiments on a real-world dataset to assess the performance of the suggested solution. The outcomes demonstrate that the suggested approach classifies garbage with a high degree of accuracy. The system can recognize and categorize various waste categories, including recyclables, organic garbage, and non-recyclables, which can then be collected individually for proper disposal. This is made possible by the application of deep learning and image processing algorithms. The suggested approach can potentially increase trash disposal efficiency, reduce waste sent to landfills, and promote a cleaner and healthier environment in metropolitan settings. The burden of garbage collection staff can be reduced, and the danger of harm can be reduced, with real-time monitoring and automated waste collection.

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Integrating deep learning, satellite image processing, and spatial-temporal analysis for urban flood prediction

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JOURNAL OF HYDROLOGY 2024年 639卷

作者： Mohamadiazar, Nasim Ebrahimian, Ali Hosseiny, Hossein Florida Int Univ Dept Civil & Environm Engn 10555 W Flagler St Miami FL 33174 USA Villanova Univ Dept Civil & Environm Engn 800 Lancaster Ave Villanova PA 19085 USA

Urban flooding is escalating worldwide due to the increasing impervious surfaces from urban developments and frequency of extreme rainfall events by climate change. Traditional flood extent prediction and mapping methods based on physical-based hydrological principles often face limitations due to model complexity and computational burden. In response to these challenges, there has been a notable shift toward satellite image processing and Artificial Intelligence (AI) based approaches, such as deep learning (DL) models, including architectures like Convolutional Neural Networks (CNN). The objective of this research is to predict near real-time (NRT) flood extents within urban areas. This research integrated CNN (U-Net) with Sentinel-1 satellite imagery, Digital Elevation Model (DEM), hydrologic soil group (HSG), imperviousness, and rainfall data to create a flood extent prediction model. To detect flooded areas, a binary raster map was created using calibrated backscatter values derived from the VV (vertical transmit and vertical receive) polarization mode of Sentinel-1 imagery, which was highlighted as having a significant impact on backscatter behavior and prediction results. Application of the model was demonstrated in urban areas of Miami-Dade County, Florida. The results demonstrated the capability of the model to provide rapid and accurate flood extent predictions at a spatial resolution of 10 m, with an overall accuracy of 97.05 %, F-1 Score of 92.49 %, and AUC of 93 % in the study area. The U-Net model's flood predictions were compared with historical floodplain data and then using GIS overlay analysis, resulting in a Ground Truth Index of 84.05 % that shows the accuracy of the model in identifying flooded areas. The research incorporated crucial flood-influencing data (including rainfall) to the flood extent prediction models and expanded the focus models beyond major rainfall events only to encompass a wider range of flood events. The presented NRT flood e

关键词： Near real -time Flood Mapping Convolutional Neural Networks (CNN) U -Net Machine learning Florida Sentinel-1

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A mechatronics data collection, image processing, and deep learning platform for clinical posture analysis: a technical note

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PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE 2021年第3期44卷 901-910页

作者： Salahzadeh, Zahra Rezaei-Hachesu, Peyman Gheibi, Yousef Aghamali, Ali Pakzad, Hamed Foladlou, Saeideh Samad-Soltani, Taha Tabriz Univ Med Sci Fac Rehabil Physiotherapy Dept 29 Bahman St Tabriz Iran Tabriz Univ Med Sci Sch Management & Med Informat Dept Hlth Informat Technol Tabriz Iran Univ Tabriz Fac Comp Engn Dept Artificial Intelligence Tabriz Iran Sanam Sahand Hlth Promot Ind Dept Res & Dev Tabriz Iran Islamic Azad Univ Tabriz Dept Biomed Engn Tabriz Iran

Static and dynamic posture analysis was a critical clinical examination in physiotherapy and rehabilitation. It was a time-consuming task for clinicians, so a semi-automatic method can facilitate this process as well as provide well-documented medical records and strong infrastructure for deep learning scenarios. The current research presents a mechatronics platform for static and real-time dynamic posture analysis, which consisted of hybrid computational modules. Our study was a developmental and applied research according to a system development life cycle. The designed modules are as follows: (1) a mechanical structure includes patient place, 360-degree engine, mirror, laser, distance meter, and cams;(2) a software module includes data collection, electronic medical record, semi-automatic image analysis, annotation, and reporting, and (3) a network to exchange raw data with deep learning server. Patients were informed about the research by their healthcare provider and all data were transformed into a Fourier format, in which the patients remained autonomous without a bit of information. The results show acceptable reliability and validity of the instruments. Also, a telerehabilitation application was designed to cover the patients after diagnosis. We suggest a longer time for data acquisition. It will lead to a more accurate and fully automated dynamic posture analysis. The result of this study suggest that the designed mechatronics device used in conjunction with smartphone application is a valid tool that can be used to obtain reliable measurements.

关键词： Posture image processing Telerehabilitation Decision support system

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A Survey on Multi-Classification of Pulmonary Diseases Using image processing and deep learning Algorithms

A Survey on Multi-Classification of Pulmonary Diseases Using...

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Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： Uma Maheswari. V Niveditha Reddy Mandala Roopika Ponnur Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

Multi-classification of pulmonary diseases poses a significant challenge, particularly when diseases share similar radiological presentations like lung cancer, pneumonia, and COVID-19. While chest CT scan images are essential for diagnosis, manual interpretation by radiologists is time-consuming and difficult, even for experts. deep learning algorithms offer promise in this domain, demonstrating high accuracy in classifying these diseases from CT images. This review examines recent advances in using deep learning for multiclassification of pulmonary diseases. It discusses various preprocessing techniques, deep learning architectures to enhance model performance. Additionally, a comparative analysis of different deep learning models on publicly available CT datasets is presented. The findings suggest that deep learning models could serve as valuable tools for multiclassification of pulmonary diseases, potentially improving diagnostic accuracy and efficiency. Nonetheless, further research is required to develop and validate these models on large, diverse datasets and ensure their robustness in real-world clinical settings.

关键词： deep learning Surveys COVID-19 image segmentation Accuracy Pneumonia Computed tomography image edge detection Lung cancer Classification algorithms

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