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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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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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A two-stage deep transfer learning model and its application for medical image processing in Traditional Chinese Medicine

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KNOWLEDGE-BASED SYSTEMS 2022年 239卷 108060-108060页

作者： Zhang, Xu Chen, Zhikui Gao, Jing Huang, Wei Li, Peng Zhang, Jianing Dalian Univ Technol Sch Software Technol Dalian Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China Dalian Med Univ Dept Crit Care Med Affiliated Hosp 1 Dalian Peoples R China

People are deepening the study on model explainability as well as performance to better understand models' decisions from the human perspective. However, the lack of rare clinical diagnosis data always limits the power of the emerging data-driven deep diagnosis methods, and the traditional deep transfer learning (DTL) applicable for the case is often insufficient to learn specific features in medical image processing, leading to poor explainability. To address those challenges, a two-stage deep transfer learning model is proposed and applied to assist in the Traditional Chinese Medicine (TCM) tongue diagnosis. Especially, a two-stage transfer learning training strategy is designed to loose the data dependence of deep learning on the domain data, which is composed of the imitate stage that discovers shared basic source features and the transfer stage to relearn target patterns, with good explainability. The corresponding deep squeeze-and-excitation convolutional network is proposed to learn the clinical patterns of tongue symptoms, in which a three-layer feature pyramid network fuses the multi-scale tongue features. Extensive experiments are conducted on the real clinical dataset in terms of classification accuracy and learning efficiency. The resulting accuracy of the proposed model proves its performance advantage with the recognition time achieving real-time performance. (c) 2021 Elsevier B.V. All rights reserved.

关键词： deep transfer learning deep neural networks Medical image processing Traditional Chinese Medicine Tongue diagnosis

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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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Human Action Recognition (HAR) using image processing on deep learning 13

Human Action Recognition (HAR) using Image Processing on Dee...

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13th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2023

作者： Ismail, Ahmad Puad Azahar, Muhammad Afiq Bin Tahir, Nooritawati Md Daud, Kamarulazhar Kasim, Nazirah Mohamat Universiti Teknologi Mara Cawangan Pulau Pinang Electrical Engineering Studies Malaysia Universiti Teknologi Mara Electrical Engineering Studies Shah Alam Malaysia

ISBN: (纸本)9798350323184

The advancement of artificial intelligence (AI) has bought many advances to human society as a whole. By using daily activities and integrating the technology from the fruits of AI, we can manage to gain further access to knowledge we can only begin to imagine. In identifying human action recognition (HAR);processing photos and videos to discern whether a human is present, then mapping the subject classified, which lastly determines the action being carried out is the objective. To achieve this, various steps are taken and careful approach is required, with the extensive amount of research, numerous troubleshooting and experimentation is required. The AI architecture has to learn from dataset collected for it to discern the identification of action properly. HAR is achieved by using Python code using real-time webcam feed. Human pose detection library known as MediaPipe Pose Detection detects human anatomy from input through joints key-points. MediaPipe algorithm that extract features in x-y-z axis with visibility (four variables) and the extracted data is trained using CNN-LSTM based on the trained and tested algorithm classifier model. The output obtained produced an RGB-skeleton and an action label on the detected subject as standing, waving, walking and sitting, has yielded good results. © 2023 IEEE.

关键词： image processing

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Diabetic retinopathy using image processing and deep learning

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2021年第4期14卷 397-409页

作者： Swain, Debabrata Bijawe, Sanket Sanjay Akolkar, Prasanna Prasad Shinde, Aditya Mahajani, Mihir Vijay Vishwakarma Inst Technol Dept Informat Technol Pune 411037 Maharashtra India

Diabetic retinopathy is one of the most non-uniform and confront regions to diagnose as it is exceptionally perplexing. In the circle of retinopathy, the number of times intensive assessments are required to be done to determine upon the diabetes mellitus or blindness that patient might be facing. Various professionals may take different amount of time to recognise diabetic retinopathy. So, a framework is required that can effectively and precisely analyse the retinal conditions with no of such limitations. This paper presents a two- stage method to effectively predict the level grading of diabetic retinopathy. The first stage involves preprocessing the retinal image and reducing the noise from an image. The second stage involves building a convolutional neural network architecture for predicting diabetic retinopathy level. It is a hurdle of diabetes that can affect the retinal nervous and lead to total or partial loss of vision.

关键词： CNN convolutional neural network diabetic retinopathy disease diagnoses deep learning machine intervention image processing artificial neural networks Gaussian blur filter Gabor filter K-means clustering ReLu Softmax

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Empirical Assessment of Identifying Human Blood Group Based on image processing Assisted deep learning Principles

Empirical Assessment of Identifying Human Blood Group Based ...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： G. Ramkumar N. Janardhana Rao V. Nanammal SK. Shaphiya J Giri Ahmad Abdelhafiz Ali Samhan Dept of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of MBA QIS College of Engineering and Technology Ongole Andhra Pradesh Department of Electronics and Communication Engineering Jeppiaar Engineering College Chennai Department of S&H QIS College of Engineering and Technology Ongole AndhraPradesh Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Department of Software Engineering Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

This study presents an empirical assessment of identifying human blood groups using image processing assisted by deep learning principles, specifically employing a cascaded Convolutional Neural Network (CNN) and Light Gradient Boosting Machine (LightGBM). Traditional methods of blood group identification are time-consuming and prone to errors, prompting the need for automated, more efficient systems. In this work, a CNN model was initially used to extract deep features from blood sample images, followed by a LightGBM classifier for final blood group classification. A comprehensive dataset, representing all major blood groups (A, B, AB, O, and Rh types), was collected and preprocessed for training and testing. The cascaded CNN-LightGBM approach achieved an accuracy of 92.4%, significantly outperforming baseline models, including standalone CNN (88.2%) and Random Forest (83.5%). The model also demonstrated high precision (92.1%), recall (92.0%), and F1-score (92.1%). real-world testing was conducted to validate its robustness in clinical settings. The results indicate that the proposed model is well-suited for accurate and efficient blood group identification, with low inference time, making it ideal for real-time applications. This approach has the potential to transform blood diagnostics by providing an automated, scalable, and accurate solution.

关键词： deep learning Training Accuracy Transforms Feature extraction Robustness real-time systems Convolutional neural networks Blood Testing

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deep learning-Driven Smart Signal Systems for Advanced image and Video processing in Urban Infrastructure

Deep Learning-Driven Smart Signal Systems for Advanced Image...

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Smart Technologies, Communication and Robotics (STCR)

作者： K. S. Bharaty Praveen SR Konduri Department of EEE School of Engineering Mohan Babu University Tirupati Andhra Pradesh Department of Data Science School of Computing Mohan Babu University Tirupati Andhra Pradesh

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

The increasing integration of intelligent systems in metropolitan environments emphasizes the important role effective image and video processing performs in applications including the traffic management, the monitoring of safety, and the urban planning. When handling dynamic scenarios, that is, changes in traffic conditions, occlusions, and the need for real-time analysis, conventional signal processing methods sometimes fail. Strong and adaptable solutions able to manage both these complexity as well as others are thus much needed. This work intends to introduce a deep learning-based smart signal system designed to process real-time video feeds for uses connected to urban traffic and safety. The proposed system uses recurrent neural networks (RNNs) for temporal sequence comprehension; conversely, convolutional neural networks (CNNs) are used for spatial data analysis. Two approaches used in order to improve the performance of the model are data augmentation and transfer learning, which increase the accuracy of decision-making using multi-modal data sources, video streams, sensor inputs, environmental parameters. This helps one to get beyond the challenge of small datasets. System performance is discovered using benchmark datasets and video from actual traffic events. Better than the methods now in use, the results show object detection has 95% accuracy and anomaly detection has 92% accuracy. Promising for use in smart cities, the proposed framework exhibits scalability and flexibility over a wide range of events. Its characteristics, traffic optimization, safety monitoring, and data-driven urban planning, which comprise traffic control, safety monitoring, and data-driven urban planning, have the ability to drastically change urban transportation infrastructure management.

关键词： Accuracy Recurrent neural networks Smart cities Urban planning Object detection Streaming media Traffic control real-time systems Safety Monitoring

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deep learning, Machine learning - Digital Signal and image processing: From Theory to Application

arXiv

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arXiv 2024年

作者： Hsieh, Weiche Bi, Ziqian Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Xu, Jiawei Wang, Jinlang Chen, Keyu Yin, Caitlyn Heqi Feng, Pohsun Wen, Yizhu Wang, Tianyang Li, Ming Ren, Jintao Niu, Qian Chen, Silin Liu, Ming National Tsing Hua University Taiwan Indiana University United States Kyoto University Japan AppCubic Rutgers University United States University of Wisconsin-Madison United States Purdue University United States Georgia Institute of Technology United States National Taiwan Normal University Taiwan University of Hawaii United States Xi’an Jiaotong-Liverpool University China Aarhus University Denmark Zhejiang University China

Digital Signal processing (DSP) and Digital image processing (DIP) with Machine learning (ML) and deep learning (DL) are popular research areas in Computer Vision and related fields. We highlight transformative applications in image enhancement, filtering techniques, and pattern recognition. By integrating frameworks like the Discrete Fourier Transform (DFT), Z-Transform, and Fourier Transform methods, we enable robust data manipulation and feature extraction essential for AI-driven tasks. Using Python, we implement algorithms that optimize real-time data processing, forming a foundation for scalable, high-performance solutions in computer vision. This work illustrates the potential of ML and DL to advance DSP and DIP methodologies, contributing to artificial intelligence, automated feature extraction, and applications across diverse domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Discrete Fourier transforms

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image processing and deep learning Based Road Object Detection System for Safe Transportation 15

Image Processing and Deep Learning Based Road Object Detecti...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Prito, Rizvee Hassan Hossain, Md. Shafayat Islam, Md. Shajibul Meem, Mehzabin Ali, Md. Nawab Yousuf East West University Computer Science and Engineering Dept Dhaka Bangladesh

ISBN: (纸本)9798350370249

Road object detection, a pivotal task in computer vision and artificial intelligence, is dedicated to the identification and precise localization of a diverse array of elements on roadways, including vehicles, pedestrians, road signs, traffic lights, and potential obstacles. The essence of this task lies in its ability to provide real-time and precise object detection, ultimately serving as a crucial safeguard to prevent accidents and ensure the safety of drivers, passengers, and pedestrians. It also lays the foundation for advanced warning systems and aids in collision avoidance. Several popular models were implemented, encompassing YOLOv7, YOLOv7-Modified, YOLOv7-Tiny, YOLOv7-E6E, Faster R-CNN, and SSD. Among these, YOLOv7 achieved an impressive mean average precision (mAP) of 83.6%, with an inference speed of 15.1 ms, while YOLOv7E6E achieved the highest mAP of 86.2%, but with the cost of a slower inference speed of 30.3 ms. The modified version of YOLOv7 has produced 2.2% higher average precision accuracy (89.1%) than the main version of YOLOv7 due to its double RepVGG layers, skip connection and concatenation layers in the head architecture. To make this research accessible to a wider audience, a user-friendly web application is developed with an intuitive interface. © 2024 IEEE.

关键词： Traffic signs

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