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Real-Time Facial Mask Detection Using Deep Learning 1

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3rd international conference on Information Technology, InCITe 2023

作者： Chauhan, Ayush Rajput, Rohan Chaudhary, Divyansh Gupta, Aakanshi Gurugram Cognizant India Noida Paytm India Tuffplast Division Suraj Logistix Pvt. Ltd Jamshedpur India Department of Computer Science and Engineering ASET AUUP Noida India

ISBN: (数字)9789819959976

ISBN: (纸本)9789819959969

The COVID-19 outbreak shook the world with its continuous waves. Since, the virus was mainly spreading through respiratory droplets, wearing a face mask was made compulsory for every person in the world with other preventive measures like taking vaccine doses. As a famous saying "Prevention of disease is still better than the cure," perfectly sums up the increase in use of face masks and even shows the importance of the masks. This research is centred around the implementation of deep learning and computer vision methodologies. Image classification has also been used on the different images of people wearing masks. Some pretrained models like MobileNetV2, VGG16, ResNet50 were used along with training a new model from scratch. Subsequently, the models were applied to a live video stream to determine whether an individual is wearing a mask correctly, incorrectly, or not at all. The use of several types of masks in various aspects and various places of society is also particularly important, wrong type of mask at wrong place can also cause severe problems. Surgical masks are used in hospitals, N95 masks in chemical labs, etc. So, it was decided to classify the masks into various categories and implement it into the model such that the model can classify the type of mask as well. To sum up the research and make the model presentable, an application was developed to make the model easy to use in different scenarios and platforms such as mobile phones, desktops, etc. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Design and Development of Gateway Based on State Grid Main control Chip

Design and Development of Gateway Based on State Grid Main C...

引用

Data Science and computer Application (ICDSCA), IEEE international conference on

作者： Xu Lei Zhou Wenfeng Tong Xiaoyu Wang Kang Beijing SmartChip Microelectronics Technology Co. LTD. Beijing China

The smart lighting gateway, developed based on State Grid Corporation China’s SCM801[1] and SCM325[2] main control chips, enables the autonomy of core devices in city-wide wide area wireless networks, reducing deployment costs and increasing access quantity. With the support of smart cities and the background of dual-carbon energy conservation, rapid promotion and application of SmartChip company’s main control chips and communication chips in the field of smart cities can be achieved. The core processor chip adopts SCM801[1], a self-developed 4-core 32BIT ARM® Cortex® A7 architecture multi-core high-performance processor by SmartChip company, with a high frequency of up to 1GHz, supporting various peripheral interfaces and hub operating systems. The wireless communication module’s transmit and receive modules are designed and implemented based on SmartChip’s self-developed SCM325[2], meeting the requirements of long communication distance, low power consumption, and secure data transmission.

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Improving the Reconstruction Efficiency of Dice- Stagewise Weak Orthogonal Matching Pursuit

Improving the Reconstruction Efficiency of Dice- Stagewise W...

引用

computer Communication and Artificial Intelligence (CCAI), international conference on

作者： Shaoqi He Liangang Xiao Shibo Gao Yichuan Zhu National KeyLaboratory and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China Committee of Science and Technologyational Key Laboratory of Science and Technology on Aerospace Beijing Aerospace Automatic Control Institute Beijing China

weak orthogonal matching pursue algorithm cannot obtain high-precision reconstructed signals in the measurement process. Thus, this study proposes an improved SWOMP algorithm called DHP-SWOMP, which is based on partial Hadamard matrix, to overcome the aforementioned shortcoming. First, Dice coefficient matching is introduced to effectively distinguish the atomic correlation and ensure the selection of the best atom for overcoming the similar atom selection in traditional SWOMP algorithm. Then, the sampling partial Hadamard matrix is proposed as the measurement matrix to overcome the issue of failing to obtain high-precision reconstructed signals when Gaussian matrix is used in SWOMP algorithm. The random independence of the matrix is used to improve the reconstruction accuracy of the algorithm. Simulation results show that the proposed algorithm improves the signal-to-noise ratio by 53.97%, shortens the reconstruction time by 87.60%, reduces the mean square error by 15.46%, and have smaller recovery residual and higher signal reconstruction rate than SWOMP algorithm based on Gaussian matrix.

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Explainable Artificial Intelligence for computer vision and Quantum Machine Learning

Explainable Artificial Intelligence for Computer Vision and ...

引用

3rd international conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Khan, Asharul Islam Al Badi, Ali Alqahtani, Mohammed OCCI HURC Sultan Qaboos University P.O.Box 33 AlKhodh MuscatP.C.123 Oman Dean Gulf College P.O.Box 885 Al Mabaila MuscatP.C.133 Oman Department of Computer Information Systems Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia

Artificial Intelligence and Machine Learning (AI/ML) are the emulation of human intelligence by computer systems. The AI/ML models have made inroads into computer vision and Quantum computing due to their tremendous ability for reasoning and problem-solving. computer vision allows computers to interpret visual information (photos, videos) similar to humans while Quantum computers execute complex tasks more efficiently than regular computers. However, how to explain the reasons for specific outputs is an important challenge with AI/ML models when applied to computer vision and Quantum computing. Without the explainability of the AI/ML, the reliability and validity of computer vision and Quantum computing outputs, when AI/ML is applied to them, is of great concern for researchers. To solve the issue of trustworthiness and transparency researchers have come up with the idea of explainable AI/ML (XAI). Therefore, to understand the extent of research on XAI in the area of computer vision and Quantum computing, this study used the existing literature from the Scopus database and statistically presented the results. The study has found that the research on XAI in computer vision and Quantum computing has been growing particularly since 2019. For AI/ML in computer vision, the maximum studies on the XAI are in the healthcare sector accounting for 57% followed by autonomous vehicles. As far as AI/ML in Quantum computing is concerned, the majority of XAI research is related to the healthcare sector accounting for 46% followed by cyber security (20%). © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Qubits

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YOLOv5-Pothole: An Improved Pothole Perception Method Based on YOLOv5-Seg 2

YOLOv5-Pothole: An Improved Pothole Perception Method Based ...

引用

2nd international conference on Artificial Intelligence, Human-computer Interaction and Robotics, AIHCIR 2023

作者： Chen, Xiaokang Ma, Nan Wang, Mohan Beijing Union University Beijing Key Laboratory of Information Service Engineering Beijing China Beijing University of Technology Engineering Research Center of Intelligence Perception and Autonomous Control Beijing China Beijing University of Technology Faculty of Information and Technology Beijing China

ISBN: (纸本)9798350360363

Road pothole damage represents a significant challenge in transportation infrastructure. Numerous studies have aimed to automate pothole detection by employing diverse computer vision techniques. There is a critical need for an automated road pothole detection process that offers sufficient accuracy and speed, while also being cost-effective and straightforward to implement. We introduce an enhanced road pothole detection algorithm, YOLOv5-pothole, based on the YOLOv5-seg model. Inputs for this algorithm include RGB images, depth images, and camera position data, all captured by a D455 depth camera. Concurrently, LiDAR sensors are used to acquire 3D point cloud data. The RGB images are semantically segmented by the self-trained YOLOv5-seg network, and corresponding virtual point clouds are generated through coordinate transformation. Then we register the 3D point clouds with the virtual point clouds generated from 2D pothole images to supplement the semantic information of the pothole. Finally, the pothole point clouds are individually segmented using Euclidean clustering, providing the information of the pothole. The GrdDC2020 experimental dataset was used to train the YOLOv5-seg network. We tested the YOLOv5-pothole algorithm on five randomly selected road potholes, with experimental results indicating a mean detection error of 7.0%. The algorithm demonstrated effective detection across potholes of varying sizes. This study offers valuable insights for future real-time road pothole detection research. © 2023 IEEE.

关键词： Object detection

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Stability analysis of uncertain neutral-type memristive neural networks with dynamic quantization

Stability analysis of uncertain neutral-type memristive neur...

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Electrical Engineering and control Science (IC2ECS), international conference on

作者： Jie Lu Lianglin Xiong School of Mathematics and Computer Science Yunnan Minzu University Kunming China

This paper investigates the problem of stability for neutral-type memristive neural networks (NMNN s) with uncertainties satisfying Lipschitz condition. The technology for dynamic quantization control input is suggested, which can reduce the burden of digital communication by utilizing quantization input. Introducing dynamic quantization parameters effectively avoids quantization saturation phenomenon. Then, using the Lyapunov- Krasovskii functional (LKF) theory and integral inequality, to assure the system's stability, new sufficient conditions are recommended. Through the analysis of the viability of solving linear matrix inequalities (LMIs), we derived the feedback gain matrix and determined a new range for the dynamic quantization parameter. Finally, we provide one numerical example with simulations to validate the usefulness of our proposed theoretical results.

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Capsule Endoscopy Image Deblurring Method Based on Multi-scale Recurrent Network 3

Capsule Endoscopy Image Deblurring Method Based on Multi-sca...

引用

2023 3rd international conference on computer, Remote Sensing and Aerospace, CRSA 2023

作者： Yang, Shiyu Guo, Tai Zhan, Ruoya Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations Harbin University of Science and Technology Heilongjiang Harbin150080 China School of Information Science and Engineering Guilin University and Technology Guangxi Guilin541004 China

During the process of acquiring capsule endoscope images, image motion blur may result from errors made by the operating physician. A multi-scale recurrent attention network is proposed to address the issue of motion blur in collected images. Considering the issue of over-exposure during image acquisition in capsule endoscopy, which can adversely affect network generation results, an image preprocessing module has been incorporated into the network to eliminate highlights caused by over-exposure in input images. To enhance network sampling efficiency, the encoder is equipped with a Convolutional Block Attention Module (CBAM) to extract fuzzy features more effectively. Additionally, depthwise separable convolution is employed to reduce parameter count. Since the structure of images obtained through capsule endoscopy is relatively simple and stable, we have opted for the mean square error as our loss function due to its heightened sensitivity to data errors. The experimental results demonstrate that the proposed network achieves a Peak Signal-to-Noise Ratio (PSNR) of 31.096 and a Structural Similarity Index Measure (SSIM) of 0.925, indicating a significant improvement over DeblurGAN-v2 and Pix2pix. © Published under licence by IOP Publishing Ltd.

关键词： Mean square error

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Optimizing Vehicle Classification using YOLOv8: Strategies to Counter Overfitting

Optimizing Vehicle Classification using YOLOv8: Strategies t...

引用

Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Anjali Jha Ishita Saxena Karuna Kadian Indu Vaibhav Sharma Shivani Singh Computer Science Department IGDTUW Delhi India Electronics & Communication Department RKGIT Delhi India

The surge in computer vision applications has amplified the need for precise and resilient car classification systems. Addressing this, our research delves into enhancing the accuracy and adaptability of systems classifying four car types: Audi, Mahindra Scorpio, Swift, and Tata Safari. Our approach used a rich training dataset of carefully curated and annotated images representing the targeted car categories, coupled with a validation dataset for model refinement. Utilizing the YOLOv8 deep learning model, we embarked on rigorous training and validation phases. A key emphasis was on combating overfitting, ensuring our model's wider applicability. Our efforts culminated in a commendable 91 % accuracy. This paper offers a gamut of insights: illustrative charts, detailed outcomes, and a thorough dissection of the confusion matrix. Our results highlight YOLOv8's prowess in car classification and its promise in bolstering object recognition systems, all while adeptly navigating overfitting challenges.

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Research on Power Load Forecasting Based on Deep Learning

Research on Power Load Forecasting Based on Deep Learning

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Lanxin Lin Jingxin Yao Kun Wang Zhejiang Gongshang University Sussex Artificial Intelligence Institute Hangzhou China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao Hebei Chinese academy of sciences University of Chinese Academy of Sciences Shenyang institute of computing technology Shenyang China

In order to fully explore the time-series correlation of power load data and improve the prediction accuracy of power load, this paper proposes a neural network-based deep learning approach for power load prediction. Firstly, the relevant electric power data are obtained and divided into appropriate sample sizes, and the samples are normalized; then, a prediction model based on LSTM is built to explore the correlation between different features, and the corresponding model of this neural network is further trained and validated on the data test set; finally, a comparison between LSTM and other algorithms such as SVM, ANN, GAOS and GM are performed. The results show that the LSTM prediction algorithm can better track the trend of power load change, with higher prediction accuracy and efficiency.

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computer vision Based Algorithms for Detecting and Classification of Activities for Fall Recognition on Real Time Video

Computer Vision Based Algorithms for Detecting and Classific...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Tirumala Kumar Kandukuru Senthil Kumar Thangavel G Jeyakumar Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Falls among the elderly population present significant risks, including serious injuries and even death. Prompt detection of falls is crucial for immediate assistance and notification to relevant parties. However, current fall detection systems face challenges and privacy concerns. To address this, a new system is proposed using a Convolutional Neural Network (CNN) and optimizing Optical Flow images through preprocessing in spatial and frequency domains. The system incorporates the Viola-Jones algorithm for human face and action recognition. It is designed for detecting falls in home or room environments, covering various postures - standing, sitting, walking, and falling. Surveillance cameras in public spaces capture relevant data for training the model. The system achieves impressive accuracy and precision, highlighting its effectiveness in fall detection applications.

关键词： Training Privacy Face recognition Surveillance Sociology Streaming media Real-time systems

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