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2022 Wave Electronics and its Application in Information and Telecommunication systems, WECONF 2022

作者： volkov, v. Yu. Saint-Petersburg Russia

ISBN: (纸本)9781665462402

The adaptive algorithm of detection and selection of isolated compact objects on monochrome images in remote sensing systems is investigated. To characterize compactness, the ratio of the object perimeter squared to its area is used. Selection of objects by area and shape is performed on binary slices in the process of multi-threshold processing, which allows you to set a detection threshold for each object, ensuring its best representation. The parameters of the algorithm, the properties of the decisive statistics are considered and detection characteristics for an object in the form of a disk under the action of Gaussian noise are obtained. The effectiveness of the algorithm is tested on a real image containing compact objects. The algorithm can also be used to isolate bacteria and spores on biological slices, to detect inhomogeneity in materials and tissues. © 2022 IEEE.

关键词： Adaptive algorithms

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Connected smart elevator systems for smart power and time saving

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Rashed, Ahmed Nabih Zaki Yarrarapu, Manasa Prabu, Ramachandran Thandaiah Raj Antony, Gnana Sagaya Edeswaran, Logashanmugam Kumar, E. Santosh Aswitha, K. Snehith, N. Ahammad, Shaik Hasane Menoufia Univ Fac Elect Engn Elect & Elect Commun Engn Dept Menoufia 32951 Egypt Prasad V Potluri Siddhartha Inst Technol Dept CSE Vijayawada India Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept ECESIMATS Chennai 602105 Tamil Nadu India Mohan Babu Univ Erstwhile Sree Vidya Nikethan Engn Coll Dept Mech Engn Tirupati 517102 Andhra Pradesh India Sathyabama Inst Sci & Technol Dept ECE Chennai 600119 Tamil Nadu India Koneru Lakshmaiah Educ Fdn Dept ECE Vaddeswaram 522302 India

Smart elevators provide substantial promise for time and energy management applications by utilizing cutting edge artificial intelligence and image processing technology. In order to improve operating efficiency, this project designs an elevator system that uses the YOLO model for object detection. Compared to traditional methods, our results show a 15% improvement in wait times and a 20% reduction in energy use. Due to the elevator's increased accuracy and dependability, users' qualitative feedback shows a high degree of pleasure. These results imply that intelligent elevator systems can make a significant contribution to more intelligent building management. Due to the elevator's increased accuracy and dependability, users' qualitative feedback shows a high degree of pleasure. These results imply that intelligent elevator systems can make a significant contribution to more intelligent building management. The successful integration of artificial intelligence (AI) and image processing technologies in elevator systems presents a promising foundation for future research and development. Further advancements in object detection algorithms, such as refining YOLO models for even higher accuracy and real-time adaptability, hold potential to enhance operational efficiency. Integrating smart elevators more deeply into IoT networks and building management systems could enable comprehensive energy management strategies and real-time decision-making. Predictive maintenance models tailored to elevator components could minimize downtime and optimize service schedules, enhancing overall reliability. Additionally, exploring adaptive user interfaces and personalized scheduling algorithms could further elevate user satisfaction by tailoring elevator interactions to individual preferences. Sustainable practices, including energy-efficient designs and integration of renewable energy sources, represent crucial avenues for reducing environmental impact. Addressing security concerns thr

关键词： Smart elevator Deep learning Power saving Time-saving image processing Floor prediction

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An Autonomous Rover for Weed Detection

An Autonomous Rover for Weed Detection

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Smart systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： Gadiyaram veda Lakshmi Durga Haritha v Naga Prudhvi Raj Gona Dayakar Bandaru Sneha Mohammad Nafisa Gadiyaram Balaji Prabhakara Sai Phaneedra Department of EIE VR Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

This project enhances agricultural productivity and promotes sustainable farming through an autonomous weed detection rover. Powered by a Raspberry Pi, the rover integrates a high-definition camera and advanced image processing algorithms to differentiate weeds from crops in real time using visual attributes like color, texture, and shape. Key components, including motor drivers and MOSFETs, enable efficient power management and autonomous navigation, reducing human intervention. The system significantly decreases manual labor by approximately 80 % while ensuring precise herbicide application, minimizing environmental impact. This scalable and efficient solution highlights the potential of intelligent automation in agriculture, fostering sustainable weed management and improving long-term crop yields.

关键词： Productivity Precision agriculture visualization Accuracy image color analysis Manuals Real-time systems Chemicals Crop yield Farming

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Deforestation Detection from Remote Sensing images using Machine Learning

Deforestation Detection from Remote Sensing Images using Mac...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Devisetty Sai Tharun Panguluri Sai Srija Peeta vamsi Krishna Shaiju Panchikkil v.M. Manikandan SRM University-AP Andhra Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The study focuses on the utilization of remote sensing data to analyze and detect deforestation patterns, with an emphasis on the extraction of key parameters such as vegetation cover change, forest loss, and land use dynamics. various image processing methods, encompassing supervised and unsupervised classification, object-based image analysis, and change detection algorithms, are discussed in the context of their applicability to deforestation monitoring. The benefits and limitations of each technique are identified, highlighting the significance of choosing the most appropriate method based on the specific needs of the study area and the required level of accuracy. The paper also explores the incorporation of remote sensing data with geographic information systems (GIS) and other ancillary data sources to enhance the analysis and interpretation of deforestation patterns. The findings from this work contribute to the advancement of remote sensing image processing methods for deforestation monitoring, offering valuable insights for researchers and practitioners in the field.

关键词： Deforestation Accuracy Soft sensors Scalability vegetation mapping Satellite images Stakeholders Remote sensing Monitoring Geographic information systems

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Overview of Autonomous Marine Surveillance Device 3

Overview of Autonomous Marine Surveillance Device

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3rd International Conference on Power, Energy, Control and Transmission systems, ICPECTS 2022

作者： Balaje, Dhivya vibha, v. Pichumani, Harish Sarkar, Swagata Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9781665462754

The use of Autonomous Underwater vehicles (AUvs) for environment exploration, data collection and research developments has been growing day by day. Beach checks and surveys are documented studies about land erosion, beach erosion, underwater mapping, and various other aspects of the marine environment. These surveys are done manually or with quad bikes, commercially coded hovercrafts, etc, that are ultimately powered by humans. We propose an autonomous beach-check survey device, making use of image processing and machine learning algorithms to inspect the entire camera in the underwater body and collect the data required. This vehicle will focus on covering all the operations to be performed which would reduce human intervention and manpower. © 2022 IEEE.

关键词： Autonomous vehicles

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Examination on Fire Detection Methods using Computer vision 1

Examination on Fire Detection Methods using Computer Vision

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1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRS 2022

作者： Sikhin, v.C. Sankara Subramanian, S. Sreelekshmi, R.v. Department of Electronics and Communication Engineering Thangal Kunju Musaliar College of Engineering Kollam India Department of Electrical and Electronics Engineering Thangal Kunju Musaliar College of Engineering Kollam India

ISBN: (纸本)9781665460842

Fire detection is important in fire alarms, firefighting robots, and other applications. Traditional techniques for detecting the presence of a fire using smoke sensors are not always effective. Fire detection has become much more effective in real time due to computer vision approaches. This study analyses various fire detection methods that make use of image processing and object detection. Furthermore, this research study compares the performance of various colour detection models such as RGB (Red;Green;Blue), HSv (Hue;Saturation;value), and YcbCr (Luminance;Chroma;Blue;Chroma;Red), as well as object detection algorithms such as Haar-cascade and YOLO (You Only Look Once) for fire detection. Moreover, this study also aims to make a breakthrough in existing fire detection methods by experimenting with colour models and object detection methods. © 2022 IEEE

关键词： Object detection

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visionary AI Enhancing Diabetic Retinopathy Detection through image processing

Visionary AI Enhancing Diabetic Retinopathy Detection throug...

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Intelligent Cyber Physical systems and Internet of Things (ICoICI), International Conference on

作者： P vinayagam S Santhiya S Naveen Kumar v Dhanush Kumar Department of ECE Saveetha Engineering College Tamilnadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The development of Diabetic Retinopathy (DR) necessitates comprehensive screening techniques to avoid visual loss. Existing manual screening techniques are subjective and prone to errors, resulting in delayed diagnosis and poor patient outcomes. In response, the paper introduces a visionary AI system that transforms DR detection via image processing methods and Convolutional Neural Networks (CNNs). The proposed system decreases the pressure on healthcare personnel by automating analyses and assuring consistency and accuracy. The proposed system outperforms existing systems, achieving 95% accuracy, 92% sensitivity, 96% specificity, and an AUC-ROC value of 0.97. Furthermore, the proposed system demonstrates scalability and adaptability, indicating a larger application in a variety of healthcare contexts. Future research aims to expand the proposed system's capabilities and incorporate it seamlessly into healthcare workflows, with a focus on its ability to greatly improve DR detection and patient outcomes.

关键词： Diabetic retinopathy visualization Accuracy Sensitivity Scalability Medical services Transforms Retina Prediction algorithms Convolutional neural networks

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Development of Lane and Zebra Crossing Marker Detection algorithms

Development of Lane and Zebra Crossing Marker Detection Algo...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： vivek Chamoli R Gowri v P Dubey Jyoti Kandpal Jyoti Joshi Divesh Kumar Dept. of Electronics & Communication Engineering Graphic Era Hill University Dehradun India Dept. of Comupter Science & Engineering Graphic Era Hill University Dehradun India

This research involves using an object recognition system and an ancillary image sensor to detect lane markers and zebra crossings on a roadway in a vehicle. To perform this detection, these steps have been followed: take a picture of the scene, digitize it, normalize it, define a search area within it, look for lane and zebra markers within it. The Lane Tracking for Driving System was created to assist drivers in making lane departure decisions, to lessen concentration breaks, and to avoid accidents while driving. To offer a way to detect a specific traffic violation—namely, stopping on a zebra crossing at a traffic signal rather than following behind it—and to offer a workaround. In the proposed work, systems are implemented and improved using adaptive algorithms. MATLAB’s image processing toolbox is used to design and implement the proposed algorithm.

关键词： image sensors image processing Adaptive algorithms Communications technology Object recognition Detection algorithms vehicles

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An improved YOLO v5-based algorithm of safety helmet wearing detection 34

An improved YOLO V5-based algorithm of safety helmet wearing...

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34th Chinese Control and Decision Conference (CCDC)

作者： Sun, Licheng Wang, Liang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China

ISBN: (纸本)9781665478960

Detecting whether workers in surveillance images and videos are following regulations to wear safety helmets plays a vital role in reducing safety accidents on a construction site. Although some recent algorithms can fulfill this task with the help of emerging deep learning techniques. The detection accuracy is still hard to meet the needs of real applications. So, a new accurate and real-time method for safety helmet wearing detection is proposed. Firstly, the YOLO v5 network is improved by combining implicit and explicit information to enhance the context-aware ability. Then, based on the improved network, an accurate, fast, and stable safety helmet wearing detection algorithm is proposed. Finally, a series of experiments are performed to validate the proposed algorithm. It shows that the proposed algorithm outperforms the state-of-the-art methods.

关键词： Safety helmet wearing detection YOLO v5 image processing Deep Learning

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Alzheimer's Disease Classification and Severity Level Identification in image processing Using Deep Learning algorithms

Alzheimer's Disease Classification and Severity Level Identi...

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Sustainable Computing and Smart systems (ICSCSS), International Conference on

作者： T Sangeetha Devi v. Raghavendran Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamilnadu India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Alzheimer's disease (AD) is a neurodegenerative condition that deteriorates brain cells and impairs a patient's memory. It is progressive and incurable. Early identification can shield the patient from more brain cell damage and, as a result, help them avoid irreversible memory loss. The scientific community has employed a number of deep learning algorithms to automatically identify Alzheimer's patients. These comprise binary classification of patient scans into stages of AD as well as moderate cognitive impairment (MCl). Limited research has been done on the multiclass classification of Alzheimer's disease (AD) up to six distinct stages. This research proposes novel technique in Alzheimer disease detection with severity level analysis utilizing deep learning (DL) model. Input is collected as MRI brain images and processed for noise removal and smoothening. Then processed image classification and disease stage is detected using pre-trained multi-layer convolutional residual transfer Random Forest with Inception v3model. Experimental analysis is carried out in terms of training accuracy, mean average mean average precision, sensitivity, AUC for various MRI brain image dataset. Training accuracy attained by proposed technique is 96%, mean average precision of 93%, sensitivity of 95%, AUC of 90%.

关键词： Deep learning Training Sensitivity Magnetic resonance imaging Noise Brain cells Brain modeling

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