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Research on Detecting and Tracking Algorithm of UAV Intrusion Based on YOLOv5+DeepSort 3

Research on Detecting and Tracking Algorithm of UAV Intrusio...

引用

3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Yang, Huaizhou Ge, Yujie Xi'an Shiyou University School of Computer Science Xi'an China

ISBN: (纸本)9781665459112

In order to accurately detect and track intruding UAVs for the phenomenon of 'black flight' and 'indiscriminate flight', this paper proposes an improved YOLOv5 algorithm combined with the DeepSort tracking algorithm, which adds the convolutional block attention module (CBAM) to the Neck module of the YOLOv5s network to enhance the extraction of network features. DIOU-NMS is introduced to improve the problem of missed detection due to the obscured UAV targets. The tracking part uses the DeepSort algorithm, in which the Kalman filter is used for predicting and updating the next frame position of the target, and DIOU is used to solve the problem of secondary matching failure. Experiments show that the proposed method can effectively identify and track UAVs, and furthermore reduce the problems of missed detection and target ID misdetection that occur after obstacle occlusion. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Few-shot Classification with First-order Task Agnostic Meta-learning 3

Few-shot Classification with First-order Task Agnostic Meta-...

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3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Yang, Xiaoxiao Xu, Jungang University of Chinese Academy of Sciences School of Computer Science and Technology Beijing China

ISBN: (纸本)9781665459112

Meta-learning approaches are typically used to solve few-shot learning tasks by training on a variety of data in the hopes of developing generalization abilities that can be applied to new tasks. Model-Agnostic Meta-Learning (MAML) is one of the popular ways for few-shot learning currently. However, the generalizability is impacted when the meta-learner is over-trained in the meta-training, leading to a bias toward existing tasks. Besides, MAML involves a second-order gradient, which costs a lot. In this paper, we propose a First-order Task-agnostic Meta-learning algorithm (TA-Reptile). Our entropy-based method utilizes a first-order gradient update to learn an unbiased model with a fine set of initialization parameters, aiming to address the difficulty of overperforming in classification tasks. Experiments on benchmark datasets illustrate that TA-Reptile has competitive performance in few-shot classification tasks. © 2022 IEEE.

关键词： Image classification

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Categorization of Butterflies using Convolutional Neural Network 3

Categorization of Butterflies using Convolutional Neural Net...

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3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Gill, Kanwarpartap Singh Sharma, Avinash Anand, Vatsala Gupta, Rupesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Haryana Mullana-Ambala133207 India

ISBN: (纸本)9781665494007

Insects make up the incredible larger part of creature species on the globe, concurring to later studies, and their numbers are quick declining. Various insect species and geological regions have detailed this event, as there may be not sufficient data to decide its full scope. Insect population examination is challenging since the majority of checking procedures are wasteful and time-consuming. On the other hand, progresses in computer vision and deep learning might before long give new arrangements to this around the world issue. Entomologists may collect information always and non-intrusively at any time of day or season utilizing cameras and other sensors. Mechanized imaging can be utilized within the lab to record the physical characteristics of examples. Utilizing this data, a deep learning demonstrate may be made to figure the number, biomass, and kind of creepy crawlies. Deep learning models can too measure the variety in phenotypic characteristics, exercises. We may thank later advancements in computer vision and profound learning for the sudden increment in order to monitor insects using sensors and using prediction models. The proposed Sequential model for this work has an accuracy of 87 percent for batch sizes of value 128 and epochs of value 100. By utilizing this highly-trained prediction calculation, this demonstration can help entomologists in creating way better forecast models that can help within the categorization of butterflies and other insects. © 2023 IEEE.

关键词： computer vision

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System Cursor control Using Human Eyeball Movement 3

System Cursor Control Using Human Eyeball Movement

引用

3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Dhanasekar, J. Guru Aravindh, K.B. Kiren, A.S. Faizal Ahamath, A. Bharath Institute of Higher Education & Research Department of Mechatronics Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350399264

This paper aims to develop a system for controlling the computer cursor using the movement of the user's eyeballs, providing a hands-free alternative to the traditional mouse. We developed a program using image processing techniques and machine learning algorithms in Python to obtain the eyeball movements and blink and translate them respectively into cursor movements and click actions. Our system was able to achieve a high level of accuracy in tracking the user's eye movements. Users were easily able to adapt to the new input method. This system has great potential to improve the accessibility and usability of computers for individuals with motor impairments or disabilities. This hands-free control method has great potential in the area of applications in gaming and virtual reality environment etc. © 2023 IEEE.

关键词： Python

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Which performs better, linear scanning or the KNN searching algorithm based on 2D grids? 3

Which performs better, linear scanning or the KNN searching ...

引用

3rd international conference on Robotics, Automation and Intelligent control, ICRAIC 2023

作者： Ji, Tong Lin, Yifeng Yang, Yuer Liu, Hui Jinan University College of Information Science and Technology Guangzhou China Jinan University College of Cyber Security Guangzhou China The University of Hong Kong Department of Computer Science Hong Kong Hong Kong University of Chinese Academy of Sciences School of Computer Science and Technology Beijing China

ISBN: (纸本)9798350360813

The K-nearest neighbor (KNN) algorithm is widely used in navigation, such as traffic management, driverless vehicles, and logistics planning. While it offers powerful instance-based learning, its performance can be influenced by data distribution and distance metric choice, and scalability can be an issue with large datasets. In this paper, we compare KNN with linear scanning in 2D grid-based search algorithms. KNN excels in accuracy, multidimensional feature processing, flexibility, and stepwise optimization, making it a strong choice for efficient and accurate 2D grid data search. Especially when the dimension of the grid is 200 ∗ 200 and the grade is 1, the performance of KNN is 99.70% better than linear scanning. However, we acknowledge that KNN's performance hinges on distance metric selection, and different metrics may perform differently in various applications. KNN is also sensitive to noise and outliers, which highlights the need for future enhancements and optimizations. © 2023 IEEE.

关键词： Large datasets

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An EEG Feature Extraction Algorithm Based on Adaptive Time-frequency Motor Imagery 3

An EEG Feature Extraction Algorithm Based on Adaptive Time-f...

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3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Wu, Yan Mao, ZhiDa Tang, LiMin Li, Qi Changchun University of Science and Technology College of Computer Science and Technology Changchun China Zhongshan Institute of Changchun University of Science and Technology Zhongshan528437 China

ISBN: (纸本)9781665459112

To solve the deficiency of traditional common spatial pattern feature extraction algorithm due to the changes of neurophysiology in subjects with the variation of time and frequency domain, a self-adaptive time-frequency domain common spatial pattern feature extraction algorithm is presented. The algorithm finds out the personalized time and frequency domain of the subject automatically by a data-driven method. The results demonstrated that the algorithm improves the performance of the traditional common spatial pattern. In addition, the regularization term of the algorithm is designed. The experiment results on two public competition data sets show that the classification accuracies of the proposed adaptive feature extraction method is higher than those of the original public model (10.9% and 20.33%). © 2022 IEEE.

关键词： Feature extraction

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

3rd International Conference on Trends in Electronics and He...

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3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

ISBN: (纸本)9789819739363

The proceedings contain 52 papers. The special focus in this conference is on Trends in Electronics and Health Informatics. The topics include: An Effective Combination of Deep and Machine Learning Models for Monkeypox Detection from Dermatographic Image;a Time-Efficient and Effective Image Contrast Enhancement Technique Using Fuzzification and Defuzzification;An Ensemble Machine Learning-Based Approach for Detecting Malicious Websites Using URL Features;The Multi-class Paradigm: How Transformers Are Reshaping Language Analysis in NLP;deep Learning Precision Farming: Identification of Bangladeshi-Grown Fruits Using Transfer Learning-Based Detection;Deep Learning Solutions for Detecting Bangla Fake News: A CNN-Based Approach;a Two-Stage Stacking Ensemble Learning for Employee Attrition Prediction;ensemble Learning Approaches for Alzheimer’s Disease Classification in Brain Imaging Data;pseudo-Knighted Cocktail Shaker Sort;sentiment Analysis in Twitter Data Using Machine Learning-Based Approach;road Object Detection for Visually Impaired People in Bangladesh;newBreeze: A Comprehensive Solution to a Beginner-Friendly Arch Linux Distribution with Zen Kernel;deep Ensemble Learning Approach for Multimodal Emotion Recognition;Tri Focus Net: A CNN-Based Model with Integrated Attention Modules for Pest and Insect Detection in Agriculture;detection and Classification of Spam Email: A Machine Learning-Based Experimental Analysis;predictive Modeling and Early Detection of White Spot Disease in Shrimp Farming Using Machine Learning: A Case Study in Bangladesh;bangla License Plate Detection and Recognition Approach Based on computer vision for Authentic Vehicle Identification;feature Techniques with a Custom Convolutional Model for Breast Tumor Surveillance in Mammograms;An AI-Based Clinical Recommendation System Using Ensemble-Based Soft Voting Classifier;machine Learning-Based Approach to Predict Heart Diseases Using Fused Dataset;an Optimal Feature Selection-Based Approach to P

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Video Abnormal Behavior Recognition and Hierarchical Warning System Based on Markov Model 3

Video Abnormal Behavior Recognition and Hierarchical Warning...

引用

3rd international conference on computer, control and Robotics, ICCCR 2023

作者： Jiang, Lingzi Zou, Beiji Liu, Shu Zhang, Yue Ji, Zexin School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665492126

In this paper, a video crowd anomaly detection and classification method based on Markov model is proposed. The anomaly detection in crowd is defined as different abnormal targets in dense crowd, and on this basis, a human feature model or a sample describing human features is established. In order to verify the effectiveness of this method, ShanghaiTech, UMN and UCSD human behavior database are used to analyze and verify the performance of the proposed Markov model feature extraction algorithm. The experimental results of video abnormal behavior recognition and detection in the above three databases using the algorithm proposed in this paper show that: The recognition rates of ShanghaiTech, UMN and UCSD human behavior databases are 94.65%, 93.46% and 90.31%. © 2023 IEEE.

关键词： Database systems

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Simple Industrial Cutting Machine Safety System Based on computer vision 3

Simple Industrial Cutting Machine Safety System Based on Com...

引用

3rd IEEE international conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Chiang, Chien-Lin Peng, Ming-Yuan Lin, I-Long Chou, Yu-Wei Fong, Ji-Men Chiang, Yi-Yuan Department of Computer Science and Engineering Tatung University Taipei Taiwan Graduate Institute of National Development and Mainland China Studies Chinese Culture University Taipei Taiwan Department of Computer Science and Information Engineering Vanung University Taoyuan Taiwan

ISBN: (纸本)9798350333862

Safety devices are often not fitted to many simple panel-cutting machines due to cost. Designing and installing low-cost safety devices on the simple panel-cutting machine improves the safety of operators. We propose a computer vision-based safety system with a camera to capture images of the operator and machine from above. Each captured image is analyzed by a neural network. If the operator's hands are too close to the cutting tool, the safety system sends a warning signal and stops the machine. The neural network used in the vision system is a ten-layer convolutional neural network. In addition to the input and output layers, there are five convolutional layers in the middle, followed by three fully connected layers. The input image undergoes five convolution operations to obtain feature maps of different granularity. With three fully connected layers, a combination of features is used to determine the positions of the operator's hands as a guideline for stopping the machine. To improve the generalization of the model, we collect thousands of training data. The experimental results show that the model has a low bias, low variance, and high accuracy characteristics. The accuracy of the verification data is over 98%. © 2023 IEEE.

关键词： Convolutional neural networks

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A Comprehensive Review Analysis of Development of Electric Vehicles in India 3

A Comprehensive Review Analysis of Development of Electric V...

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3rd international conference on Power Electronics and IoT Applications in Renewable Energy and its control, PARC 2024

作者： Koli, Ankush Mundotiya, Prahlad Garg, Joohi Parmar, Bhoomika Tiwari, Harpal Malaviya National Institute of Technology Department of Electrical Engineering Jaipur India Mody University of Science and Technology Dept. of Electronics & Comm. Engg Sikar India Bikaner Technical University Department of Computer Science Engineering Bikaner India

ISBN: (纸本)9798350309409

One of the major challenges in the world is to control the impacts of global warming and greenhouse gas emissions on the environment. To reduce CO2 emission, an environment friendly substitute for traditional fossil fuel-powered automobiles is produced as electric vehicles. A comprehensive analysis of the growth of electric vehicles is carried out in this study as they are amenable to reducing global emissions. The motivations and developments that shaped India's EV policy trajectory till 2023 have been examined in this research. The primary factors of EV policy include that cars contribute to the decline of urban ambient air quality, international climate change accords, reduced import costs for crude oil, and the need for sustainable urban transportation options. Given the budget-conscious Indian market and phased deployment, the EV transformation must be made more feasible and accessible while maintaining economic viability. The evolution of EV policy in India has been studied in this article. © 2024 IEEE.

关键词： Electric vehicles

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