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Yolo algorithms to improve the detection of personal protective equipment thought industrial computer vision 30

Yolo algorithms to improve the detection of personal protect...

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30th IEEE international conference on Electronics, Electrical Engineering and Computing, INTERCON 2023

作者： Velasquez, Ricardo Arias Lara, Jennifer Vanessa Mejia Velasquez, Renato Arias Universidad Tecnológica Del Perú Lima Peru Universidad Nacional de San Agustín de Arequipa Lima Peru

ISBN: (纸本)9798350315578

During 2023, accidents at work are unexpected situations that cause losses in both human and material resources. In Peru there is current legislation that encourages a safe work environment, however, according to 2019 reports from the Ministry of Labor and Employment Promotion, 31.92% of accidents caused this year directly affected the cranial area. the use of personal protective equipment requires finding an optical way to identify the required safety equipment in real time. In response to this problem, the use of artificial vision or computer vision for helmet recognition. the trained model for online personal protective equipment classifier algorithms with computer vision in industrial applications allows to detect helmets, head, safety glasses, industrial vests with precision metrics greater than 88% according to light intensity. Additionally, it is possible to improve the accuracy of the other clusters by training with higher epochs, increasing batch processing from eight to sixteen, and using parallel processing. Despite the fact that the configuration profile for the training process carried out was basic, the database used, due to the varied labeling and number of images, acceptable results were obtained. Also, due to the selection of the YOLOV5m model, transfer learning training contributed to the training time without reducing the accuracy rates. Our findings is the recognition of a model with 5000 pictures with an accuracy of 88% from 400 to 800 lux and limited for the light intensity lower than 400 lux, with 78.63%. With light intensity higher than 800 lux, the recognition time of less than 0.3 seconds/image together with a rate of 98% with a low-cost camera and parallel processing with a Raspberry Pi version 4. © 2023 IEEE.

关键词： computer vision

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4th international conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th international conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249709

the proceedings contain 113 papers. the special focus in this conference is on Applied Technologies. the topics include: Automatic Evaluation of Physiotherapy Activities Using Deep Learning Techniques;multivariable Control Approach Applied to the Embryo Incubation Process of Gallus Gallus Domesticus;discovering Visual Deficiencies in Pilots Candidates Using Data Mining;an Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital image processing and Neural Networks;artificial Intelligence-Based Banana Ripeness Detection;U-Net vs. TransUNet: Performance Comparison in Medical image Segmentation;access Control through Mask Detection and Estimation of People Capacity in Covered Premises;customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules;Bibliometric Analysis of Web of Science Database STEM Fields in Engineering and Mathematics. Ecuador’s Case Study;smart Antenna Array for Optimal Electromagnetic Energy Capture;image processing Method to Estimate Water Quality Parameter;use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised Machine Learning Model Using Facial recognition;crime Data Analysis Using Machine Learning Models;Gene therapy as a Solution to Genetic Diseases through DNA Manipulation;Vulnerability of CAPTCHA Systems Using Bots with computer vision Abilities;military Leadership in the Ecuadorian Army;analysis of Key Variables for Ecuadorian Defense Industry Development;the Prioritization of External Security as a Means of Guaranteeing Multidimensional Security and Economic Growth;Comparative Study of Deep Learning Algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages;ground Robot for Search and Rescue Management;virtual Training Module for the Production of Rubber Adhesives through the Production of Cyclopentenol.

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4th international conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th international conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249778

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Generative Adversarial Networks for Domain Translation in Unpaired Breast DCE-MRI Datasets 4th

Generative Adversarial Networks for Domain Translation in Un...

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4th international conference on Deep Learning theory and Applications (DeLTA)

作者： Galli, Antonio Gravina, Michela Marrone, Stefano Sansone, Carlo Univ Naples Federico II Dept Elect Engn & Informat Technol DIETI Naples Italy

ISBN: (纸本)9783031390586;9783031390593

Generative Adversarial Networks (GAN) are more and more gaining attention in the computer vision domain thanks to their ability to generate synthetic data, in particular in the context of domain adaptation and image-to-image translation. these properties are attracting the medical community too, in order to solve some complex biomedical challenges, such as the translation between different medical imaging acquisition protocols. Indeed, as the actual acquisition protocol is strongly dependent on factors such as the operator, the aim, the centre, etc., gathering cohorts of patients all sharing the same typology of imaging is an open challenge. In this paper, we propose to face this problem by using a GAN to realise a domain translation architecture in the case of breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI), considering two different acquisition protocols, in the context of automatic lesion classification. Despite this work wanting to be a first step toward artificial data generation in the medical domain, the obtained results have been analysed from both a quantitative and qualitative point of view, in order to evaluate the correctness and quality of the proposed architecture as well as its usability in a clinical scenario.

关键词： Breast MRI Generative adversarial networks image-to-image translation

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Smart vision and Intelligent Object recognition System for the Visually Impaired 15th

Smart Vision and Intelligent Object Recognition System for t...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Suresh, Merugu Sandhyarani Shaik, Abdul Subhani Premalatha, B. Ghinea, George Reddy, Avala Raji Raju, K. Srujan Kumar, Voruganti Naresh Department of ECE CMR College of Engineering and Technology Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India Mulsemedia Computing Brunal University London Wilfred Brown Building 215 Uxbridge United Kingdom

ISBN: (纸本)9783031810824

In an effort to increase the functional dependence for the visually impaired people, have identified the disadvantages and drawbacks in the present existing solutions and have tried to include the loopholes in the existing systems. Identified and addressed the problems like: object recognition, identification of packaged goods, identification of currency coins and notes, ability to read texts recognize people and faces. this paper provides complete support to the visually impaired friends. the idea of this paper is an augmented reality wearable spectacle along with a wearable smart vision band on the wrist. the Spectacles are equipped with camera, earphones, GPS module and Internet connection (Wi-Fi &4G). the wearable smart vision system contains ultrasonic sensors, IR sensors to recognize the objects that are present before/behind the visually impaired person and can alert using a vibration. the Spectacles and the band are connected using Bluetooth. the outcome of the assistive device should be in the form of audible signal received using the earphones, that can be clearly usable for a visually impaired friend. this research paper works on the principle of computer vision for image processing using OpenCV and Google Cloud vision API. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image coding

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Implementing Open Source Biometric Face Authentication for Multi-factor Authentication Procedures 11th

Implementing Open Source Biometric Face Authentication for M...

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11th international conference on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production (HPCST)

作者： Minakova, Natalya Mansurov, Alexander Altai State Univ Lenin Ave 61 Barnaul 656049 Russia

ISBN: (纸本)9783030941413;9783030941406

this study proposes a solution that extends the capabilities of web information systems with single-factor authentication by introducing an additional authentication factor based on biometric face recognition. the proposed solution design and its main operation steps are presented and discussed. the solution utilizes the standard multimedia functionality of popular web browsers and supports available or built-in image capturing devices (photo and web cameras). Robust program algorithms from the open source computer vision library are used for face image processing and analysis. Experimental testing and validation of the algorithms for face localization and recognition are conducted with image sets produced with consideration of reality. Experimental results demonstrate high effectiveness with a success rate of 80% ... 93% for the solution based on the local binary pattern face localization algorithm with the local binary pattern histogram face recognition algorithm.

关键词： Biometrics Face authentication Face recognition computer vision Local binary pattern

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Integration of Artificial Intelligence and Internet of things Technology in Classroom Attendance Systems. 7

Integration of Artificial Intelligence and Internet of Thing...

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7th international conference on image Information processing, ICIIP 2023

作者： Firdous, Faisal Bashir, Saimul Rufai, Syed Zoofa Kumar, Sanjeev Computer Science And Engineering Juit HP solan India Chandigarh University Computer Science And Engineering Punjab India Sgt University Computer Science And Engineering Punjab Gurugram India

ISBN: (纸本)9798350371406

computer vision technology has been increasingly applied in various utility apps, including facial recognition. While the accuracy of facial recognition may not be as high as that of fingerprint, iris, or RFID recognition is still used due to its non-contact recognition process. In light of the advantages of facial recognition, an automated attendance solution for smart classrooms has been proposed. this system integrates an embedded device with AI and IoT technologies to continuously take attendance throughout the time period of the class. the system's main components include an embedded device with an attached camera sensor, a Cloud server for data storage and analysis, and an instruction to replace old data with newly recorded data at the beginning of each roll call According to the results of the experiment conducted on the proposed system, each frame of the system has an accuracy rate of 89%, and it can recognize faces within a range of 4-15 meters at a speed of 82 ms per face. this has low operating costs and can be deployed rapidly. © 2023 IEEE.

关键词： Operating costs

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Chess Piece image recognition Using Transfer Learning, Simple Neural Network, and Convolutional Neural Network 4

Chess Piece Image Recognition Using Transfer Learning, Simpl...

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4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Yohanes, Gabriel Nursalim, Mario Nicholas Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

Online chess has opened up a way for players to sharpen their skills through move analytics. Based on this feature, a support system called chess advisor can be utilized to assist players in a real-time match. However such system doesn't exist within the website itself, rather an involvement of 3rd party software is required. these kinds of software usually need assistance for input. this paper aims to create a system that is able to recognize chessboard state in real-time. We have proposed a chess piece image recognition system using transfer learning using ResNet50, a simple neural network, and a convolutional neural network (CNN). We collected our own 693 2D chess piece images dataset and performed data preprocessing to standardize the image dimensions. the proposed system detects and converts individual positions of each piece into a computer-readable output. this output can be used to facilitate the chess advisor in determining the best move. the transfer learning approach utilizing the ResNet50 model exhibited superior performance with an accuracy of 95.65%. this outperformed alternative methods such as the simple neural network and the CNN. the ResNet50 model's advantage stemmed from its pre-training on the imageNet dataset, enabling effective pattern generalization. In contrast, the simple neural network and CNN achieved lower accuracies of 73.4% and 85%. these models encountered difficulties due to limited dataset quantity and quality, as well as the absence of regularization techniques. Nevertheless, the CNN displayed better consistency in generalizing patterns compared to the simple neural network. © 2023 IEEE.

关键词： image recognition

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PoLaNet: Head Pose Estimation and Face Landmarks Detection Network 4

PoLaNet: Head Pose Estimation and Face Landmarks Detection N...

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4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Duo, Yunfeng Zhao, Xiong Faculty of Computer Information Kunming Metallurgy College Kunming China Key Laboratory of IMI in Yunnan Universities Yunnan College of Business Management Kunming China Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia

ISBN: (纸本)9798350324303

In this paper, We proposed an improved efficient structure named PoLaNet for head pose estimation and landmarks detection from a single image while maintaining continuous representation. which transforms the Head Pose Estimation (HPE) problem into a problem of directly predict continuous 6D rotation matrix parameters belongs 3D Special Orthogonal Group (SO(3)), then use 3D Morphable Models (3DMM) to predict the landmarks and combined the two loss to enhance the accuracy. the model uses the light MobileNet-V2 as the backbone, and the improved combining loss function. Experiments show that compared with the original SOTA methods, our model parameters were reduced by about 83.3%, and the accuracy improved about 8%. © 2023 IEEE.

关键词： image enhancement

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Design of product visual defect detection system based on computer vision with AI 4

Design of product visual defect detection system based on co...

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4th international conference on image processing and Intelligent Control, IPIC 2024

作者： Li, Haige Song, Caili Wu, Hongqi Research Affairs Office Xi'an Vocational University of Automobile Shaanxi Xi'an China School of Computer Science Xi'an Petroleum University Shaanxi Xi'an China School of Electronic and Information Engineering Xi'an Vocational University of Automobile Shaanxi Xi'an China

ISBN: (纸本)9781510682313

To ensure product quality, it is necessary to detect the appearance defects of products to ensure that high-quality products enter the market. Machine deep learning technology is used to extract product defect characteristics, and parameters are adjusted through small sample detection to realize the identification training of the detection system. AI algorithm is used to conduct real-time defect positioning on the production line, which can meet the requirements of product defect detection, to control the production line for correct distribution. In the production process, with the increase of defect samples, the defect samples produced in the production are directly annotated and retrained, which can gradually improve the detection accuracy of the network model and enhance the generalization performance of the network model. After the system design is completed, the production line is tested to achieve a high detection accuracy. In addition, when detecting two similar targets, we only need to change the data set and training parameters and use the transfer learning method to load the previously trained weights into the new model for training, to achieve good results. © 2024 SPIE.

关键词： Machine vision

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