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Vehicle Detection in Challenging Scenes Using CenterNet Based Approach

Computers, Materials & Continua 2023年第2期74卷 3647-3661页

作者： Ayesha Muhammad Javed Iqbal Iftikhar Ahmad Madini OAlassafi Ahmed SAlfakeeh Ahmed Alhomoud Department of Computer Science University of Engineering and Technology Taxila47050Pakistan Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589Saudi Arabia Faculty of Computing and Information Technology Northern Border UniversityRafhaSaudi Arabia

Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System(ITS)and reduces road *** major obstacles in automatic detection of tiny vehicles are due to occlusion,environmental conditions,illumination,view angles and variation in size of *** research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed *** this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks(DNN)namely *** DNN disregards objects that are small in size 5 pixels and more false positives likely to happen in crowded *** there are two categories of deep learning models single-step and two-step.A single forward pass model is the one in which detection is performed directly to possible location over dense sampling,wherein two-step models incorporated by Region proposals followed by object *** in this research scrutinize one-step State of the art(SOTA)model CenteNet as proposed recently with three different feature extractor ResNet-50,HourGlass-104 and ResNet-101 one by *** train our model on challenging KITTI dataset which outperforms in comparison with SOTA single-step technique MSSD300∗which depicts performance improvement by 20.2%mAPandSMOKEby with 13.2%mAP *** of CenterNet can be justified through the huge improved *** performance of our model is evaluated on KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute)benchmark dataset with different backbones such as ResNet-50 gives 62.3%mAP ResNet-10182.5%mAP,last but not the least HourGlass-104 outperforms with 98.2%mAP CenterNet-HourGlass-104 achieved high mAP among above mentioned feature *** also compare our model with other SOTA techniques.

关键词： CenteNet SOTA object detection deep learning MSSD SMOKE DNN KITTI

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Vision-Based Automated Parking Monitoring Using Geo-Tagged UAV Images

Vision-Based Automated Parking Monitoring Using Geo-Tagged U...

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2024 International Conference on Green Energy, computing and Sustainable Technology, GECOST 2024

作者： Lai, Chung Yi Jo, Hudyjaya Siswoyo Lai, Chean Hung Dunn, Michelle Swinburne University of Technology Sarawak Campus Faculty of Engineering Computing and Science Kuching Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn Campus Melbourne Australia

ISBN: (纸本)9798350357905

This paper proposes an automated solution for parking monitoring using geo-tagged UAV (Unmanned Aerial Vehicle) images. The parking monitoring system utilizes a UAV to patrol the parking lot. The UAV captures video footage that contains geolocation information. Geo-tagged images are extracted from the video footage and processed using the YOLOv8 object detection algorithm to recognize occupied and vacant parking spaces. The license plates of the parked vehicles in the occupied parking space are extracted using EasyOCR and stored in a database. Each parking space is labeled with a unique ID with its date, time, geolocation data, and occupancy status to facilitate integrated digital parking solutions for both the users and parking operators. © 2024 IEEE.

关键词： License plates (automobile)

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Survey on Video Security:Examining Threats,Challenges,and Future Trends

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Computers, Materials & Continua 2024年第9期80卷 3591-3635页

作者： Ali Asghar Amna Shifa Mamoona Naveed Asghar Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science College of Science and EngineeringUniversity of GalwayGalwayH91 TK33Ireland

Videos represent the most prevailing form of digital media for communication,information dissemination,and ***,theirwidespread use has increased the risks of unauthorised access andmanipulation,posing significant *** response,various protection approaches have been developed to secure,authenticate,and ensure the integrity of digital *** study provides a comprehensive survey of the challenges associated with maintaining the confidentiality,integrity,and availability of video content,and examining how it can be *** then investigates current developments in the field of video security by exploring two critical research ***,it examine the techniques used by adversaries to compromise video data and evaluate their *** these attack methodologies is crucial for developing effective defense ***,it explores the various security approaches that can be employed to protect video data,enhancing its transparency,integrity,and *** compares the effectiveness of these approaches across different use cases,including surveillance,video on demand(VoD),and medical videos related to disease ***,it identifies potential research opportunities to enhance video data protection in response to the evolving threat *** this investigation,this study aims to contribute to the ongoing efforts in securing video data,providing insights that are vital for researchers,practitioners,and policymakers dedicated to enhancing the safety and reliability of video content in our digital world.

关键词： Attacks threats security services video manipulation video security

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A learning automata based edge resource allocation approach for IoT-enabled smart cities

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Digital Communications and Networks 2024年第5期10卷 1258-1266页

作者： Sampa Sahoo Kshira Sagar Sahoo Bibhudatta Sahoo Amir H.Gandomi Department of Computer Science and Engineering C.V.Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering SRM UniversityAmaravatiIndia Department of Computing Science Umea UniversityUmea90187Sweden Department of Computer Science and Engineering NIT RourkelaIndia Faculty of Engineering and Information Technology University of Technology SydneyAustralia University Research and Innovation Center(EKIK) Obuda University1034 BudapestHungary

The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled smart *** high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for ***,there is a high computation latency due to the presence of a remote cloud *** computing,which brings the computation close to the data source is introduced to overcome this *** an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay *** efficient resource allocation at the edge is helpful to address this *** this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation ***,we presented a three-layer network architecture for IoT-enabled smart ***,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization *** Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource *** extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

关键词： Edge computing IoT Learning automata Resource allocation Smart city

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Toward AI-Augmented Formal Verification: A Preliminary Investigation of ENGRU and Its Challenges

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IEEE Access 2025年 13卷 84357-84379页

作者： Dechsupa, Chanon Panboonyuen, Teerapong Vatanawood, Wiwat Padungweang, Praisan So-In, Chakchai Khon Kaen University College of Computing Department of Computer Science Khon Kaen400002 Thailand Chulalongkorn University Faculty of Engineering Department of Computer Engineering Bangkok10300 Thailand

State-space graphs and automata serve as fundamental tools for modeling and analyzing the behavior of computational systems. Recurrent neural networks (RNNs) and language models are deeply intertwined, as RNNS provide the foundational architecture that enables language models to process sequential data, capture contextual dependencies, and improve natural language processing tasks. Both RNNs and state-space graphs help evaluate discrete-time systems within this formal framework. However, the basic question of their equivalence remains an open challenge regarding the models governing sentence structure in natural language and the formal model in automata theory. In this paper, we present ENGRU (Enhanced Gated Recurrent Units), a novel deep learning-based approach for formal verification. ENGRU integrates concepts of model checking techniques, Colored Petri Nets (CPNs), and sequential learning to analyze systems at a high level of abstraction. CPN models undergo state-space enumeration via a model-checking tool, generating a state-space graph and an automaton from inherent state transition patterns. These graphs are transformed into sequential representations as sub-paths, enabling ENGRU to learn the execution paths and predict system behaviors. ENGRU effectively predicts goal states within discrete-time models by the competency of gated recurrent mechanisms, encouraging early bug detection and allowing predictive state-space exploration. Experimental results demonstrate that ENGRU’s ability achieves high accuracy and efficiency in goal state predictions. © 2013 IEEE.

关键词： Model checking

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Ecofriendly autonomous smart walker for enhanced ambulatory assistance

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IEEE Potentials 2024年第3期43卷 23-30页

作者： Asante, Isaac Theng, Lau Bee Tsun, Mark Tee Kit Chua, John Swinburne University of Technology Faculty of Engineering Computing and Science Sarawak Kuching93350 Malaysia Swinburne University of Technology Sarawak Campus Sarawak Kuching93350 Malaysia

The global demographic landscape is undergoing a significant transformation, characterized by a rising elderly population and an increasing number of people requiring rehabilitation and ambulatory assistance. This study introduces an innovative, ecofriendly smart walker designed to assist physically disadvantaged individuals in navigating their environment safely and independently. The smart walker features continuous user tracking;autonomous return-to-user navigation;mecanum wheels;and motors facilitating movement, significantly reducing leg efforts. It operates in two modes, manual and automatic, and is equipped with an Intel RealSense D455 camera, Raspberry Pi, and Arduino Mega 2560, among other components. It additionally comes with fail-safe measures for user safety. © 1988-2012 IEEE.

关键词： Robots

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HealthScan AI-Deep Learning-Based Multi-Disease Diagnosis from Medical Imaging

HealthScan AI-Deep Learning-Based Multi-Disease Diagnosis fr...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Ganesh, Prudhvi Sai Kiriti, Chakali Murthy Gari Keerthi Rama, P. Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India College of Engineering and Technology SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies TN Kattankulathur603203 India

ISBN: (纸本)9798350375237

The HealthScan AI is a comprehensive health diagnosis AI system which utilizes deep learning algorithms and the Keras library to determine if a person has certain diseases or not using their chest x-rays and other scans. It utilizes six distinct CNN models for the identification of COVID-19 and pneumonia using the chest X-rays, Glaucoma from internal eye scans. Our methodology involves rigorous training and evaluation processes, resulting in a highly accurate and efficient model. The integration of these models into a user-friendly interface, developed with Streamlit, allows seamless interaction. This interface empowers users to upload medical images, facilitating real-time diagnostic feedback on their health status. By providing a flexible and effective tool for the identification of a range of disorders, this novel technique advances the field of medicine and has the potential to completely transform healthcare procedures. © 2024 IEEE.

关键词： COVID-19

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The limitation of machine learning methods for water supply and demand forecasting: A case study for Greater Melbourne, Australia

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Water Supply 2024年第11期24卷 3848-3861页

作者： Mohammadi, Maryam Gato-Trinidad, Shirley Kuok, Kuok King Department of Civil and Construction Engineering Swinburne University of Technology HawthornVIC3122 Australia Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Kuching Sarawak93350 Malaysia

Many cities around the world have faced water scarcity due to climate change, population growth, and urbanization. Accurate water supply and demand forecasting is critical for sustainable urban water management. Machine learning (ML) models provide new possibilities for forecasting compared with traditional models in handling non-linearity. This study aims to address the efficacy of ML models, long short-term memory (LSTM), stacked LSTM, bidirectional LSTM (Bi-LSTM), and multilayer perceptron (MLP), for forecasting water supply and demand in Greater Melbourne, Australia. The ML modelling utilized daily water supply and demand, and climatic variables (rainfall and maximum temperature) recorded by Melbourne Water and the Bureau of Meteorology from 1990 to 2019. The stacked LSTM performs better than other models in forecasting with R2, RMSE, and MAPE values of 0.908, 335.74 ML, and 23.5% for water supply and 0.791, 94.88 ML, and 5.3% for water demand, respectively. The inclusion of climatic variables enhanced the accuracy of forecasting by improving R2 and reducing RMSE and MAPE. The results indicate effectiveness of ML models, particularly LSTM-based architectures, in forecasting water supply and demand. However, these models have limitations, particularly in forecasting extreme values, emphasizing the need to improve ML models for more reliable and accurate water management. © 2024 The Authors.

关键词： Demand response

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A proposed strut efficiency factor for strut-and-tie design of steel fibre-reinforced concrete

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Materials Today: Proceedings 2022年 64卷 1054-1060页

作者： Ng, L.T. Tang, A.C.S. Looi, D.T.W. Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak 93350 Malaysia

Strut-and-tie model (STM) is a lower-bound plastic theory to achieve force equilibrium. It uses a simplified truss analogy method to define the load transfer mechanism in the disturbed region (D-region) in concrete structures. STM was mostly applied in the design of conventional reinforced concrete (RC), and there were few discussions on the use of STM for newer concrete technology, e.g., steel fibre-reinforced concrete (SFRC). SFRC presents opportunities in reducing web reinforcements in serviceability checks and in improving the ductility of D-regions. In this paper, a strut efficiency factor for STM is proposed for the design of SFRC by collecting a database of SFRC deep beams. It was discovered that the strut efficiency factor best fits with a function of the product of the fibre factor and concrete cylinder compressive strength. Baseline deep beam numerical models were developed and calibrated with the experimental load-displacement graphs collected through literature. A parametric study with variables such as concrete cylinder compressive strengths, fibre volumes and strut angles were investigated. The proposed strut efficiency factor was found to have a peak capacity lower than the ultimate load capacity computed in the calibrated numerical models, which justified the safe use of STM as a lower-bound plastic method in SFRC. © 2022

关键词： Compressive strength

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Telugu Language Analysis with XLM-RoBERTa: Enhancing Parts of Speech Tagging for Effective Natural Language Processing

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SN Computer science 2025年第2期6卷 1-13页

作者： Bharathi Mohan, G. Prasanna Kumar, R. Krishna Jayanth, K. Doss, Srinath Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Faculty of Engineering and Technology Botho University Gaborone Botswana

This research investigates the efficacy of XLM-RoBERTa, a potent deep learning architecture rooted in transformer networks, for Part-of-Speech (POS) tagging—a foundational task in Natural Language Processing (NLP). The study narrows its focus to address the formidable UCD Telugu dataset, renowned for its intricate morphological characteristics that pose challenges to accurate tagging. Through meticulous fine-tuning of the pre-trained XLM-RoBERTa model. This study adeptly discerns and exploit intricate syntactic and semantic patterns underpinning the interplay between lexical elements and their respective POS tags. The rigorous evaluation regimen, complemented by a comprehensive statistical comparison with the Multilingual BERT model, conclusively establishes XLM-RoBERTa’s superiority. This advanced model attains a remarkable accuracy score of 91%, decisively surpassing the Multilingual BERT model, which achieves an accuracy of 88%. These findings not only underscore XLM-RoBERTa’s prowess in precise POS tagging, especially in the context of morphologically rich languages like Telugu, but also carry significant implications for the broader NLP community. This research unlocks valuable insights and positions XLM-RoBERTa as an exemplar of reliability and robustness in POS tagging. Furthermore, it extends an invitation for further exploration and innovation in deep learning architectures within the NLP domain. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bert Multilingual Bert NLP Parts-of-speech tagging Telugu XLM-RoBERTa

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