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Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

Intelligent Automation & Soft Computing 2023年第4期36卷 911-927页

作者： Kisaezehra Muhammad Umer Farooq Muhammad Aslam Bhutto Abdul Karim Kazi Department of Computer Science and Information Technology NED University of Engineering&TechnologyKarachi75270Pakistan Department of Civil Engineering NED University of Engineering&TechnologyKarachi75270Pakistan

The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime *** in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every *** of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are *** most cases,this leads to serious brain injuries in people present at the site in general and the workers in *** is one of the leading causes of human fatalities at construction *** the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-deﬁned personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not ***,such monitoring through technological aids or automated tools is very much *** present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in *** can indicate whether a worker is wearing a hat or *** proposed system usesﬁve different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 *** results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

关键词： Object detection computer-vision personal protective equipment(PPE) deep learning industry revolution(IR)4.0 safety helmet detection

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Smoke detection in foggy surveillance environment using parallel vision transformer network

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Neural Computing and Applications 2024年第31期36卷 19499-19514页

作者： Chaturvedi, Shubhangi Thakur, Poornima Singh Khanna, Pritee Ojha, Aparajita Computer Science and Engineering Department PDPM Indian Institute of Information Technology Design and Manufacturing Dumna Road Madhya Pradesh Jabalpur482005 India

Recent years have seen an unprecedented increase in fire incidents, resulting in severe damage to forest regions, loss of human and animal lives, and unwarranted displacement of people. Owing to these issues, artificial intelligence-based fire detection systems have been developed. These systems mostly work with camera-based inputs for fire detection and alarm generation. As fire-smoke can be seen from a distance, methods for smoke detection have also been deployed for identifying fire incidents. But detecting smoke in an outdoor environment can be challenging due to weather conditions such as haze, fog, or clouds. These challenges have led to advancements in the field, and various deep learning architectures have been suggested over the years for fire-smoke detection in normal and foggy or cloudy weather conditions. Many existing methods are either computationally expensive and demand high memory, or if they are lightweight, they suffer from lower smoke detection accuracy and high false alarms. In the present paper, a dual-channel vision transformer model ‘SmokeViT’ is proposed for detecting fire-smoke in an outdoor environment. These channels are interlaced with a convolutional neural network to improve the feature learning capability of the model. ‘SmokeViT’ shows a remarkable accuracy of more than 99% and false alarm rate below 0.20% on two publicly available smoke datasets, outperforming six state-of-the-art methods. The model architecture has only 0.4 million parameters and 0.17 giga floating-point operations, making it suitable for deployment on resource-constrained surveillance devices. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Smoke

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Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

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computers, Materials & Continua 2023年第3期74卷 5507-5525页

作者： Subhajit Chatterjee Yung-Cheol Byun Department of Computer Engineering Jeju National UniversityJeju-si63243Korea Department of Computer Engineering Major of Electronic EngineeringJeju National UniversityInstitute of Information Science&TechnologyJeju63243Korea

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient *** kickboards are gradually growing in popularity in tourist and education-centric *** the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is *** to its freefloating nature,the shared electric kickboard is a common and practical means of *** plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is *** demand accurately with small data is *** data is necessary for training machine learning algorithms for effective *** generation is a method for expanding the amount of data that will be further accessible for *** this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original *** proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction *** modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster ***,we applied a regression-based blending ensemble technique that can help us to improve performance of demand *** used various evaluation criteria and visual representations to compare our proposed model’s *** data generated by our suggested GAN model is also *** TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.

关键词： Machine learning generative adversarial networks electric vehicle time-series TGAN WGAN-GP blend model demand prediction regression

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Spatiotemporal crowds features extraction of infrared images using neural network

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2543-2556页

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Drieieva, Hanna Meleshko, Yelyzaveta AlRawashdeh, Hazim Al-Oraiqat, Karim A. Hasan, Yassin M. Y. Maricar, Noor Khan, Sheroz Department of Computer Science College of Engineering and Information Technology Onaizah Colleges Qassim Saudi Arabia Department of Cybersecurity and Software Central Ukrainian National Technical University Kropyvnytskyi Ukraine Department of Artificial Intelligence Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering College of Engineering and Information Technology Onaizah Colleges Qassim Saudi Arabia

Crowds can lead up to severe disasterous consequences resulting in fatalities. Videos obtained through public cameras or captured by drones flying overhead can be processed with artificial intelligence-based crowd analysis systems. Being a hot area of research over the past few years, the goal is not only to identify the presence of crowds but also to predict the probability of crowd-formation in order to issue timely warnings and preventive measures. Such systems will significantly reduce the probablity of the potential disasters. Developing effective systems is a challenging task, especially due to factors such as naturally occuring diverse conditions, variations in people or background pixel areas, noise, behaviors of individuals, relative amounts/distributions/directions of crowd movements, and crowd building reasons. This paper proposes an infrared video processing system based on U-Net convolutional neural network for crowd monitoring in infrared video frames to help estimate the people crowd with normal or abnormal trends. The proposed U-Net architecture aims to efficiently extract crowd features, achieve sufficient people marking-up accuracy, competitively with optimal network configurations in terms of the depth and number of filters to consequently minimise the number of coefficients. For further faster processing, hardware resources/implementation area savings, and lower power, the optimized network coefficients measured are represented in Canonic-Signed Digit with minimal number of nonzero (± 1) digits, minimizing the number of underlying shift-add/subtract operations of all multipliers. The achieved significantly reduced computational cost makes the proposed U-Net effectively suitable for resource-constrained and low power applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Exploring Disease Insight Among Patients with Depression: The Role of Alcohol Consumption Frequency as a Mediating Factor 6

Exploring Disease Insight Among Patients with Depression: Th...

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6th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Lin, Ting-Yan Tsai, Meng-Feng Huang, Chi-Sheng National Central University Department of Computer Science and Information Engineering Taoyuan City Taiwan National Taichung University of Science and Technology Department of Computer Science and Information Engineering Taichung City Taiwan

ISBN: (纸本)9798350396133

In contemporary society, bipolar disorder and depression have become prevalent mental health issues. Despite patients often experiencing severe emotional and behavioral disturbances, a lack of self-awareness and insight into their illness prevents many from recognizing their mental health problems early. Currently, most academic research focuses on the relationship between depression and patients' insight or the association between depression and alcohol use behavior. However, there is a lack of literature examining alcohol use behavior as a mediating factor and exploring the causal relationships among these factors. Therefore, this study aims to investigate the causal relationships among these three variables, with the severity of depression as the exposure, insight as the outcome, and frequency of alcohol consumption as the mediating factor. Additionally, we employ regression analysis as the primary analytical method. The results indicate that: (1) an increase in the severity of depression decreases awareness of insight;(2) the severity of depression not only directly affects insight but also indirectly reduces awareness of insight through the frequency of alcohol consumption. These findings are significant for enhancing our understanding of patients' cognitive conditions and formulating relevant intervention measures. © 2024 IEEE.

关键词： Regression analysis

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A Multi-Objective Clustered Input Oriented Salp Swarm Algorithm in Cloud Computing

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computers, Materials & Continua 2024年第12期81卷 4659-4690页

作者： Juliet A.Murali Brindha T. Department of Computer Science and Engineering Noorul Islam Centre for Higher EducationKumaracoil629180India Department of Information Technology Noorul Islam Centre for Higher EducationKumaracoil629180India

Infrastructure as a Service(IaaS)in cloud computing enables flexible resource distribution over the Internet,but achieving optimal scheduling remains a *** resource allocation in cloud-based environments,particularly within the IaaS model,poses persistent *** methods often struggle with slow opti-mization,imbalanced workload distribution,and inefficient use of available *** limitations result in longer processing times,increased operational expenses,and inadequate resource deployment,particularly under fluctuating *** overcome these issues,a novel Clustered Input-Oriented Salp Swarm Algorithm(CIOSSA)is *** approach combines two distinct strategies:Task Splitting Agglomerative Clustering(TSAC)with an Input Oriented Salp Swarm Algorithm(IOSSA),which prioritizes tasks based on urgency,and a refined multi-leader model that accelerates optimization processes,enhancing both speed and *** continuously assessing system capacity before task distribution,the model ensures that assets are deployed effectively and costs are *** dual-leader technique expands the potential solution space,leading to substantial gains in processing speed,cost-effectiveness,asset efficiency,and system throughput,as demonstrated by comprehensive *** a result,the suggested model performs better than existing approaches in terms of makespan,resource utilisation,throughput,and convergence speed,demonstrating that CIOSSA is scalable,reliable,and appropriate for the dynamic settings found in cloud computing.

关键词： Cloud computing clustering resource allocation scheduling swam algorithms optimization common with in the subject discipline

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Behavioral insights into compression: a study of Move-to-Front-or-Middle deterministic online algorithm through sequence classification and characterization

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International Journal of information technology (Singapore) 2025年第1期17卷 189-203页

作者： Baisakh Mohapatra, Hitesh Guru, Abhishek Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Sambalpur India Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to Be University Bhubanseswar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Since the list update problem was applied to data compression as an effective encoding technique, numerous deterministic algorithms have been studied and analyzed. A powerful strategy, Move-to-Front (MTF), involves moving an accessed item to the front of the list immediately. Theoretical analysis has demonstrated MTF’s potential, showing it to be 2-competitive through competitive analysis. This success has inspired researchers to develop various intriguing MTF variants and assess their performance both theoretically and experimentally. One such variant, Move-to-Front-or-Middle (MFM), has been experimentally shown that MFM performed better than the classic MTF on Calgary and Cantebury Datasets. However, the experimental performance alone does not provide sufficient insight into the internal behavior of the algorithm or explain why its performance surpasses MTF on certain datasets. Therefore, in this paper, we conduct a detailed behavioral study of MFM through sequence classification and characterization to gain deeper understanding of the algorithm and aims to reconcile the disparity between theoretical and experimental outcomes. This paper also by classifying and characterizing MFM’s performance across various request sequences, considering both the presence and absence of locality of reference. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Competitive analysis Deterministic list update algorithm List update problem Sequence classification

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Genetic Algorithm Based Deterministic Workflow Scheduling with Load Balancing 2

Genetic Algorithm Based Deterministic Workflow Scheduling wi...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Mittal, Punit Kumar, Satender Sharma, Swati Department of Computer Science and Engineering Quantum University Roorkee India Department of Information Technology Meerut Institute of Engineering and Technology Meerut India

ISBN: (纸本)9798331530389

A crucial problem in cloud computing is load balancing, which makes it challenging to guarantee that services operate as intended in accordance with quality of service (QoS), performance reviews, and service contracts that cloud service providers (CSPs) demand of businesses. The main goal is to distribute workloads among computer resources in order to improve performance. Load balancing is categorized as an "NP-hard" problem due to the ample solution space and the time needed to identify the optimal solution. While only a few approaches may provide an exact answer in polynomial time, prior research shows that metaheuristic-based tactics can find correct solutions in a fair amount of time. We use a framework that integrates Neural Network Task Classification (N2TC) and Genetic Algorithm Task Assignment (GATA) for load balancing. By taking load balancing and task completion time into account, this approach seeks to maximize load distribution and reduce process execution time. Extensive simulations of application operations were carried out to evaluate the effectiveness of the suggested load-balancing strategy. Results have shown that the proposed framework outperforms tournament selection genetic algorithm (TSGA), Heterogeneous Earliest Finish Time (HEFT), and Performance Effective Task Scheduling (PETS) heuristics in terms of load balancing up to 4.52% and makespan by 6.47%. © 2025 IEEE.

关键词： Genetic algorithms

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A Meticulous Range of Forensics Tech Advancement in the field of IoT, IIoT and MIoT 5

A Meticulous Range of Forensics Tech Advancement in the fiel...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Matta, Priya Kamalraj, R. Saxena, Kumud Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Computer Science Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

Wide application of millions of connected, intelligent, and flexible gadgets in critical infrastructures like medical facilities, public transit, sustainability, and home automation has been driven by latest expansions in intelligence technology and hardware. Clients profit from consistency and accessibility when data is transmitted across the internet deprived of the need for any kind of person to person or person-to-computer communication however this additionally creates new prospects for hackers and increases a number of complicated and challenging issues for the area of online forensics. Regardless of the fact that IoT records may be a valuable evidence source, forensics professionals must deal with a wide range of issues, starting the massive diversity of IoT devices and unrecognized setups to the multi-tenant platforms of cloud and the ensuing multijurisdictional lawsuits. Encryption at all levels, which compromises users' rights regarding confidentiality for the benefit of forensics study, is another difficulty. The aim of this work is to recognize and address the issues connected through the complex procedure of IoT based inquiries, particularly the privacy and security problems. The study also examines the application of IoT forensics across different industries. © 2024 IEEE.

关键词： IoT (Internet of things) IoT forensics privacy issues and digital forensics Security issues

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A Customizable Virtual Assistant Enhanced with Vision and Speech Capabilities 12th

A Customizable Virtual Assistant Enhanced with Vision and Sp...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Naveen Kumaran, R. Sanjay Abhishek, J. Bhuvana, S. Karthick, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering SRM Institute of Science and Technology Uttar Pradesh Delhi–NCR Campus Ghaziabad India

ISBN: (纸本)9789819788354

Recent advancements in Smart Assistants (SAs) as well as home automation have captured the attention of both researchers and consumers. Virtual Assistants (VAs) that are speech-enabled are commonly referred to as smart speakers. They offer a wide range of network-based services. In some cases, they seamlessly link to smart environments, introducing innovative and efficient user interfaces. Nevertheless, these devices present their own set of challenges and limitations. The distinctive features of this technology, particularly its hand-free operation handled by voice and implementation of voice-user interface create a unique landscape. Existing technology adoption models, however, fall short in offering a comprehensive explanation for the adoption of this innovative technology. To gain deeper insights into the motivation behind approving and using in-home voice assistants, this work shifts its focus towards examining voice interactions. Utilization of speech recognition for converting speech into text enables constructing an attractive personalized assistant. This development facilitates seamless tasks such as sending emails without manual typing, conducting Google searches without opening the browser, and accomplishing various daily activities including playing music or launching a preferred IDE, all with a single voice command. To assess its performance, the system is seamlessly integrated with home automation (open-source) environment and operated continuously for some days. During this period, users are encouraged to interact with it, and the results demonstrate increased accuracy, reliability and appeal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech recognition

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