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EM2-YOLO: Lightweight Remote Sensing Image Detection 5th

EM2-YOLO: Lightweight Remote Sensing Image Detection

引用

5th International conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Liu, Xinyi Song, Yaolian Wang, Can School of Information Engineering and Automation Kunming University of Science and Technology Yunnan650500 China

ISBN: (纸本)9789819751808

Remote sensing photographs have a wealth of information, and object detection methods are crucial in this. On mobile and embedded platforms, deep learning-based target identification algorithms are challenging to implement. Additionally, the issues of delayed target detection and poor accuracy of small target detection for remote sensing photos must be resolved immediately. To address these issues, we provide EM2-YOLOv4, a simple method for recognizing remote sensing images. MobileNetv2 acts as the backbone network to reduce model complexity, while depth-separable convolution enhances detection speed. Additionally, we introduce a feature enhancement module and an Efficient Channel Attention (ECA) module to improve model accuracy. To address the issue of unequal assignment of positive and negative samples, we incorporate Focal Loss into the loss function. Experimental demonstrate that the model can perform detection at 45 frames per second with an average accuracy of 94.11%. The model has been significantly reduced, as seen by the fact that it is only 48 MB in size, but it still maintains good detection accuracy and accelerates detection. The model is ideal for usage on embedded devices because of this feature. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： optical remote sensing

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optical Character Recognition Based Answer Script Correction Using Deep Learning and Language processing algorithms

Optical Character Recognition Based Answer Script Correction...

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Ubiquitous Computing and Intelligent information systems (ICUIS), International conference on

作者： Sankara Mahalingam M N. Suresh Kumar Basireddy Sai Charan Reddy Nalibili Venkata Suneel Kumar Reddy Adimulam Govardhan Harshith Sai Bontha Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Tamilnadu India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

The rapid advancements in digital assessment systems have highlighted the need for efficient, automated methods of evaluating written examination scripts. The paper presents a new framework that leverages optical Character Recognition (OCR), deep learning, as well as Natural Language processing (NLP) to automate the correction and evaluation of handwritten answer scripts. The suggested system begins by utilizing OCR to digitize handwritten responses, converting them into machine readable text. Subsequently, advanced learning frameworks, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are employed to classify and recognize written characters with high accuracy, even in varying handwriting styles. Once digitized, the responses undergo semantic analysis and content evaluation using advanced NLP algorithms. These algorithms, integrated with transformer models like BERT (Bidirectional Encoder Representations from Transformers), evaluate the coherence, relevance, and correctness of answers by comparing them to model answers and predefined keywords. The system incorporates both syntactic and semantic matching techniques to ensure a comprehensive assessment of the answer content, while also analyzing grammar and language use. The proposed system can significantly streamline the educational assessment process, paving the way for intelligent and automated grading systems in academic and professional environments.

关键词： Deep learning Handwriting recognition Accuracy optical character recognition Semantics Transformers Ubiquitous computing Natural language processing Classification algorithms Convolutional neural networks

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SinhaLearn: NLP, CNN, and OCR Based Data Driven Approach for Enhancing Sinhala Proficiency of Grade 5 Scholarship Students 10

SinhaLearn: NLP, CNN, and OCR Based Data Driven Approach for...

引用

10th International Multidisciplinary Engineering Research conference on Moratuwa Engineering Research, MERCon 2024

作者： Francis, Juliana V Bellanavithana, Yasiru L Pulasinghe, Thisal K Silva, Rashmika D Rathnayake, Samadhi Karunathilaka, Supipi V Sri Lanka Institute of Information Technology Department of Computer Science Malabe Sri Lanka

ISBN: (纸本)9798331529048

The Sinhala language, deeply rooted in Pali and Sanskrit, holds immense cultural significance for the Sinhalese community in Sri Lanka. However, its intricate morphology and diglossic nature, where written and spoken forms diverge, pose significant challenges. In response, we present 'SinhaLearn' an automated system designed specifically to enhance Sinhala language proficiency for the Grade 5 Scholarship examination domain. Our system leverages advanced Natural Language processing algorithms, optical Character Recognition technology, Convolutional Neural Networks, and Part-ofSpeech tagging. Key functionalities include Sinhala Handwriting Capturing and Recognition, real-time error detection and correction in spelling and grammar within Sinhala Present Tense sentences, simplified explanations for complex Sinhala vocabulary aligned with the Grade 5 curriculum, and automated assessment of handwritten responses with instant feedback and scoring. Notably, informed by a comprehensive literature review, our research has made significant improvements by blending rule-based methods with hybrid components, effectively addressing critical gaps in the field. While existing systems often concentrate on singular functionalities such as spelling analysis, grammar analysis, dictionary functions, or content display, our 'SinhaLearn' system provides a holistic solution by integrating these aspects. This comprehensive approach sets a new benchmark for automated systems in Sinhala language proficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Rural road environment segmentation of LiDAR dataset with deep learning 13

Rural road environment segmentation of LiDAR dataset with de...

引用

13th International conference on Signal processing systems, ICSPS 2021

作者： Zakria, Z. Deng, Jianhua He, Jiani Cai, Jingye Khokhar, Muhammad Saddam School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

Unstructured road segmentation is a key task in self-driving technology and it's still a challenging problem. Mostly available point cloud datasets focus on data collected from urban areas, and approaches are evaluated for structured roads or urban areas, which has considerable limitations in rural areas such as fails at night, road without boundary lines, and no markings. In this regard, we present a new large-scale aerial LiDAR dataset of rural roads with hand-labeled points spanning 500 km2 of road and nine object categories. Our dataset is the most extensive dataset contains a critical number of expert-verified hand-labeled points for analyzing 3D deep learning algorithms, allowing existing algorithms to shift their focus to unstructured road data. The nature of our data, the annotation methodology, and the performance of existing state-of-the-art algorithms on our dataset are all described in detail. Furthermore, challenges and applications of rural area road semantic segmentation are discussed in detail. © COPYRIGHT SPIE.

关键词： Data acquisition

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Recognition and Analysis of Glass Subsurface Defects by Integrating Machine vision and Image processing Technology

Recognition and Analysis of Glass Subsurface Defects by Inte...

引用

Computers, information processing and Advanced Education (CIPAE), 2020 International conference on

作者： Chen Lin Xiong Ling Guangming Luo Wuhan University of Science and Technology Wuhan China Changli Glass Honghu Co. Ltd Wuhan China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

Machine vision and computer image processing technologies are widely used in the metallurgical industry, especially in recognizing and analyzing defects in glass. High surfaces of planer surface and quality in the glass and metal manufacturing sector primarily require automated and performance visual detection, and inspection systems and algorithms are constantly improving. This article, therefore, seeks to comprehensively recognize and analyze glass subsurface defects through the integration of image processing technologies and machine vision. According to the algorithm's properties and the image features typified in the process, the defects of identified glass components are primarily classified based on determining the cause and the optical features of characteristics exhibited. Given the nature of varied and detected effects determined mainly by the classifiers, an improved accuracy level was notable, and a reduction of the computational complexity was evident following the use of machine vision and image processing technologies.

关键词： visualization Image recognition Machine vision Metals Glass optical imaging Classification algorithms Manufacturing Surface treatment optical devices

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Rapid identification of rumors based on BERT and CNN

Rapid identification of rumors based on BERT and CNN

引用

2024 International conference on Computer Application and information Security, ICCAIS 2024

作者： Zhu, Huaizhong Ran, Lina Li, Wanlu Zhao, Yanni Wang, Lisha Ling, Xufeng Department of Computer Science Shanghai Normal University Tianhua College Shanghai201815 China Department of Information Shanghai Customs College Shanghai201204 China

ISBN: (纸本)9781510689312

Rumor detection is a process designed to identify and analyze false or misleading content in information. The rapid development of social media is an important channel for people to obtain external information, and also a platform for the widespread dissemination of false news. In view of this phenomenon, this paper analyzes the key elements such as time, place, people and events in the news text information by algorithm, and uses the algorithm to analyze the social media, multiple information such as news sources, historical data, and comprehensively analyze the authenticity of the news between the two. This paper mainly introduces the steps and methods of Bidirectional Encoder Representations from Transformers model and Bidirectional Encoder Representations from Transformers-Convolutional Neural Network model in capturing text information, and tests and optimizes the algorithm. The test results show that these two algorithms can efficiently and quickly identify most of the fake news, and intuitively show them to users through the system output, which is worth applying and popularizing in the corresponding field. © The Authors.

关键词： Natural language processing systems

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Design of Power Grid Full-service Data Monitoring Platform Based on Cloud Computing 2

Design of Power Grid Full-service Data Monitoring Platform B...

引用

2nd International conference on algorithms, Microchips, and Network Applications, AMNA 2023

作者： Li, Wei Zhang, Li Duan, Junxiang Cao, Lu Guo, Tao Customer Service Center of State Grid Tianjin300306 China Beijing China-Power Information Technology Co. Ltd. Beijing100031 China

ISBN: (纸本)9781510664791

The use of power monitoring and management system in power enterprises has significantly improved the convenience, fault tolerance, and related economic income of power grid in power supply. At present, there are still many problems in the management system, such as the lack of unified planning. Many functions related to power monitoring and management often need a lot of software to be repeatedly installed in the system to complete the functions required by customers. Meanwhile, this software often has duplicate functions. Power data can not be shared, and the upgrade of the system will encounter many obstacles. To meet the data communication requirements of component-based power monitoring and management system, this paper applies the characteristics of multi-point distribution and real-time and efficient transmission of data based on model-based information transmission. It also applies component-based data monitoring technology to complete the design. For the power data content of monitoring and calculation, a reusable framework is applied to facilitate user expansion. Users can complete the interface layout by dragging controls on the control interface of the power monitoring and management system, and then realize data customization and saving by binding the database. Finally, the monitoring of the power grid data platforms under cloud service is realized, which is conducive to the effective and timely processing of blowout power grid data. It is of great significance to the safe and stable operation of the power system. © 2023 SPIE.

关键词： Distributed database systems

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Predicting Frags in Tactic Games using Machine Learning Techniques and Intuitive Knowledge

Predicting Frags in Tactic Games using Machine Learning Tech...

引用

2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Lin, Chang State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350313154

High-quality prediction models can be used for game analysis and help to create challenging and human-like artificial intelligence (AI) agents. In this Predicting Frags in Tactic Games grand challenge, we apply our intuitive knowledge of video game for feature engineering, and propose a sequential floating forward and backward method combined with gradient boosting decision tree (GBDT) algorithm for feature selection. We generate different feature subsets which represent different aspects of the problem to train several GBDT models, and use the ensemble of these models for frag prediction. Our methods can be considered as a collection of heuristic rules and have well interpretability. They can act as heuristic evaluation functions that properly approximate the outcomes, which are critical to accelerating solution discovery in artificial intelligence search algorithms. The effectiveness of our methods was proven by experiments and competition results. © 2023 IEEE.

关键词： Decision trees

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visual SLAM for Dynamic Environments Based on Static Key-Points Detection 9

Visual SLAM for Dynamic Environments Based on Static Key-Poi...

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9th International conference on virtual Reality, ICVR 2023

作者： Zhong, Liangtao Zhang, Xiaoqiang Ran, Lingyan Han, Yamin Chu, Hongyu Southwest University of Science and Technology School of Information Engineering Mianyang China Northwestern Polytechnical University School of Computer Science Xi'an China Northwest A&f University College of Information Engineering Xianyang China

ISBN: (纸本)9798350345810

Recently, the technique of virtual reality has drawn great attention from both academics and industry fields. In many VR applications, the time-varying orientations and the positions of the VR headset are needed, where the technique of simultaneous localization and mapping (SLAM) can be applied. While great progress has been made in vision-based SLAM algorithms and systems in the recent two decades, the problem of visual SLAM in dynamic environments with moving objects is still unaddressed. In this paper, a static key-point detection approach is proposed based on optical flow feature clustering and potential moving region detection and is utilized for RGB-D SLAM in a dynamic environment. Firstly, considering the fact that there could be multiple moving objects in the scene, an optical flow feature is designed to cluster the multiple motion patterns in the scene. Secondly, an ultra-lightweight object detection network is utilized to detect potential moving regions in the scene, without using GPU resources. Finally, by utilizing the key-point clustering and potential moving regions detection, the static key-points are detected and inputted into the keypoint-based visual SLAM methods. The proposed approach is easy to insert as a pre-processing module in various key-point based visual SLAM systems. Extensive experimental results with qualitative and quantitative evaluations have demonstrated the performance with satisfactory accuracy of the proposed approach in various challenging dynamic scene scenarios. © 2023 IEEE.

关键词： Object detection

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A Survey on fast and incremental technologies for big data 2

A Survey on fast and incremental technologies for big data

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2022 2nd conference on High Performance Computing and Communication Engineering, HPCCE 2022

作者： Wei, Fengmei Jiang, Shuqiang Wang, Kechao College of Information Engineering Harbin University 109 Zhongxing Street Heilongjiang Harbin China College of Computer Science and Technology Harbin Engineering University 145 Nantong Street Heilongjiang Harbin China College of Intelligent Systems Science and Engineering Harbin Engineering University 145 Nantong Street Heilongjiang Harbin China

ISBN: (纸本)9781510663374

Big data is characterized by large amount of quantity, complex structure and real time performance. The main difficulty in processing big data is to improve the processing speed and accuracy. In this paper, some fast and incremental technologies are introduced, which usually used to increase the processing speed of the big data. And this paper also illustrates the application fields of these technologies. In order to speed up the query and classification of big data, index technique is proposed. The application of indexing technique can optimize candidate data sets, which could be used in data clustering and reachable query problems;In view of the increasing dynamic data, an incremental technique is used to process the big data. This method only considers the data that has changed, rather than re-modeling all the data each time as traditional methods did. Compared with traditional algorithms, incremental technique could get faster speed and higher accuracy in data prediction and graph query;In order to process the mixed big data quickly, some mixed frame for big data are given in this paper. The main mixed frames include flow-batch mixing frame and graph-flow fusion frame, which based on mixed data processing technique. At last, some experiments are given to prove the feasibility of these techniques. © 2023 SPIE.

关键词： Big data

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