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Oriented Object Detection in Remote Sensing image based on YOLOV5 14

Oriented Object Detection in Remote Sensing Image Based on Y...

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14th international conference on signal Processing systems, ICSPS 2022

作者： Pan, Dan Wu, Lifang Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (纸本)9798350336313

In recent years, remote sensing satellites have developed rapidly and accumulated massive high-resolution image data. Using deep learning to solve remote sensing image object detection has become an important research direction. However, due to shooting from a bird's eye view, the object has an oblique Angle. Because of the perspective problem, remote sensing images have boundary problem when the object is near horizontal. When there are a large number of densely tilted object boxes, using horizontal box detection method will cause box occlusion thus missing or mislabeling. Therefore, it is a challenging task to solve remote sensing image object detection. In this paper, we propose the network structure which based on YOLO-V5 and combined with CSL method. Moreover, the loss function is improved to deal with the overlapping problem of tilted object boxes effectively. As revealed by extensive experiments, the average accuracy of this method on DOTA data sets shows a competitive advantage. At the same time, the size of the network is only 16.3m, which is obviously better than the comparison method. © 2022 IEEE.

关键词： Object detection

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4th international conference on 3D Imaging Technologies- Multidimensional signal Processing and Deep Learning, 3D IT-MSP&DL 2022

4th International Conference on 3D Imaging Technologies- Mul...

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4th international conference on 3D Imaging Technologies- Multidimensional signal Processing and Deep Learning, 3D IT-MSP and DL 2022

ISBN: (纸本)9789819912292

The proceedings contain 51 papers. The special focus in this conference is on 3D Imaging Technologies- Multidimensional signal Processing and Deep Learning. The topics include: Research on the Application of 3D Modeling technology in Mechanical Structure Teaching;2D Numerical Model Used to Investigate the Influence of Vegetation on Geomorphological Evolution of Mudflats;design and Experimental Verification of High Functional Density Cubesat System;space Design of Exhibition Hall based on Virtual Reality;Identification of Expressway Traffic States based on the Enhanced FCM Algorithm;algorithms Applied in Soft Tissue Deformation Simulation: A Survey;soft Tissue Cutting based on Position Dynamics;research on Application technology of Multi-source Fusion of 3D Digital Resources in Power Grid;rapid Identification of Herbaceous Biomass based on Raman Spectrum Analysis;a Knowledge Representation Method for Hierarchical Diagnosis Decision-Making Under Uncertain Conditions;research on Spam Detection with a Hybrid Machine Learning Model;research on Weld Defect Object Detection based on Multi-channel Fusion Convolutional Neural Network;Design and Research of Highway Tunnel Construction Positioning System based on UWB;research on Cross Domain Data Analysis and Data Mining technology of Power Grid Digital Benefits;graph Convolutional Neural Networks for Drug Target Affinity Prediction in U-Shaped and Skip-Connection Architectures;SOC Estimation of Lithium Titanate Battery based on Variable Temperature Equivalent Model;research and Implementation of Hybrid Storage Method for Multi-source Heterogeneous Data of Electric Distribution Network;Research on Measuring Method of Ball Indentation based on MATLAB image Edge Detection;multi-feature Extraction of Mineral Zone of Tabling Through Deep Semantic Segmentation.

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DID-IDS: A Novel Diffusion-based Imbalanced Data Intrusion Detection System 11

DID-IDS: A Novel Diffusion-based Imbalanced Data Intrusion D...

引用

2023 IEEE 11th international conference on Information, Communication and Networks, ICICN 2023

作者： Zhang, Wenxin Chen, Zijie Chen, Dongwei Li, Jianqing Pan, Yuanyuan Macau University of Science and Technology School of Computer Science and Engineering China Zhuhai China

ISBN: (纸本)9798350314014

With the continuous expansion of the volume of internet, the methods and forms of network attacks have also improved and developed, which requires advanced intrusion detection system. Although deep learning methods achieve remarkable success in the accuracy of intrusion detection over traditional methods, a great number of balanced data are required in the training process. However, in the real-life circumstance, the ratio between benign and abnormal data is not balanced. To address this issue, this paper proposes a novel intrusion detection system, called Diffusion-based Imbalanced Data Intrusion Detection System (DID-IDS). In this system, in order to enhance the correlation between data features, the table data are firstly transformed into the image data. Then, the converted images are sent to the diffusion model to generate high-quality fake images for data enhancement purposes. Finally, the enhanced data set is used to train the Convolutional Neural Network (CNN) model for intrusion detection to improve prediction accuracy. The experimental results show that the proposed system has better performance compared to intrusion detection systems using traditional or Generative Adversarial Networks (GAN) based data augmentation methods. © 2023 IEEE.

关键词： Deep learning

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4th international conference on 3D Imaging Technologies- Multidimensional signal Processing and Deep Learning, 3D IT-MSP&DL 2022

4th International Conference on 3D Imaging Technologies- Mul...

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4th international conference on 3D Imaging Technologies- Multidimensional signal Processing and Deep Learning, 3D IT-MSP and DL 2022

ISBN: (纸本)9789819911448

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Enhancing IoT-Edge Computation with Data Forwarding based Decentralized Deep Neural Networks 7

Enhancing IoT-Edge Computation with Data Forwarding based De...

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7th international conference on image Information Processing, ICIIP 2023

作者： Prasad, B.M.G. Jopate, Rachappa Savita, Pankaj Basi Reddy, A. Shankar, B. Prabu Arunkumar, M.S. Department of Cse Andhra Pradesh Kurnool518452 India University of Technology And Applied Sciences - Al Mussanah Department of Information Technology Al Muladha Muscat Oman Sagar Institute of Science & Technology Bhopal Organization-Sagar Institute of Science & Technology Department of Cse Madhya Pradesh Bhopal India Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India Alliance University Alliance College of Engineering And Design Department of Computer Science And Engineering Karnataka Banglore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology Department of Computer Science And Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371406

The incorporation of edge computing has become essential in the big data and internet of Things age to tackle issues related to latency, real-time processing, and data scale. This paper delves into the imperative need for edge computing within the context of big data environments. Distributed deep learning methodologies are explored, encompassing a diverse array of deep learning models. Particularly, a novel paradigm named Data Forwarding based Decentralized Deep Neural Network (DF-DDNN) is introduced to achieve low latency IoT-Edge computation. In order to reduce latency in Io T ecosystems, the DF-DDNN model makes use of edge devices' processing capabilities. By strategically distributing data processing and computation at the edge, this model seeks to overcome inherent limitations of conventional Io T architectures, with a keen focus on network, processing, and overall latencies. The research provides a thorough performance evaluation that compares the DF-DDNN model's performance versus conventional IoT models in a number of areas. The outcomes demonstrate how much better the suggested DF-DDNN model is than traditional IoT systems. Notably, improvements are evident in terms of reduced network latency, accelerated processing speeds, and overall latency mitigation. This research underscores the critical importance of embracing edge computing in the context of big data and deep learning applications. The DF-DDNN model emerges as a promising avenue for advancing IoT-Edge computation, effectively addressing latency concerns and augmenting the performance of IoT-enabled systems. © 2023 IEEE.

关键词： Edge computing

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A Non-Local Means Algorithm-based Raman-OTDR for High-Performance Distributed Temperature Sensing 15

A Non-Local Means Algorithm-based Raman-OTDR for High-Perfor...

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15th international conference on Information Optics and Photonics, CIOP 2024

作者： Yan, Ziheng He, Jun Du, Bin Xu, Xizhen Xu, Baijie Li, Zhuoda Wang, Yiping Key Laboratory of Optoelectronic Devices and Systems Ministry of Education/Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Photonic Devices and Sensing Systems for Internet of Things Guangdong and Hong Kong Joint Research Centre for Optical Fibre Sensors Shenzhen University Shenzhen518060 China

ISBN: (数字)9781510686175

ISBN: (纸本)9781510686168

Raman-optical-time-domain-reflectometry (R-OTDR) based distributed temperature sensing (DTS) is of great significance in extremely harsh environment. In this work, we propose a non local means (NLM) algorithm for R-OTDR performance improvement without modifying the hardware architecture. The NLM algorithm real-time reconstruct the two-dimensional temperature image by utilizing a first-in-first-out data transmission protocol. These images are incorporated to mitigate the complexity of calculating Euclidean distances between neighborhoods within two-dimensional spatial-temporal domains. This approach significantly improves the response speed of the system. To verify the sensing performance improvement of the NLM, we built a standard R-OTDR system and affirm the seamless continuity of temperature variations spanning a broad range from 40 °C to 80 °C over an extensive distance of 9.68 km assisted by a standard platinum resistor. The temperature accuracy decreases from 1.4 °C to 0.55 °C at sensing temperature of 100 °C. The results effectively reconcile the conventional trade-off between high temperature resolution and high-speed response. What’s more, an average temperature resolution is 0.05 °C at the end of fiber. In all, the Raman DTS system based on NLM algorithm achieve high temperature resolution. This achievement holds immense potential for diverse applications, including dynamic monitoring in the realms of energy development, oil and gas exploration, allied fields, and so on. © 2024 SPIE.

关键词： Optical fibers

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LUT-based Area-Optimized Accurate Multiplier Design for signal Processing Applications 4th

LUT-Based Area-Optimized Accurate Multiplier Design for Sign...

引用

4th EAI international conference on Cognitive Computing and Cyber Physical systems, IC4S 2023

作者： Satyanarayana, B.V.V. Lakshmi, B. Kanaka Sri Kumar, G. Prasanna Srinivas, K. Department of ECE Vishnu Institute of Technology Bhimavaram534202 India

ISBN: (纸本)9783031488870

Multipliers play a role in various aspects of smart cities, which can be used in many applications like Traffic management, energy management and environmental management etc. The wide variety of applications of multipliers are in the field of signal processing and image processing. FPGA design of multiplier is one of the complex tasks in Digital electronics. Most of the designs uses DSP blocks, these multipliers are complex and occupies much area in FPGA. Accurate multiplier design with low area on FPGA is the challenging task. The proposed method is accurate multiplier design, which is designed only using lookup table (LUT). The proposed design has low power and reduced area because of using simple LUT’s for generating partial product. The proposed accurate multipliers reduce 10% less Hardware on vertex 7 FPGA compared to existing designs. © 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Field programmable gate arrays (FPGA)

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Network Intrusion Detection based on Federated Learning with Inherited Private Models 5

Network Intrusion Detection Based on Federated Learning with...

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5th international conference on Video, signal and image Processing, VSIP 2023

作者： Feng, Yanxiang Zhang, Enyan Zhang, Yang Zeng, Zhen Wang, Aili Heilongjiang Province Key Laboratory Of Laser Spectroscopy Technology And Application Harbin University Of Science And Technology Harbin150080 China State Grid Heilongjiang Electric Power Co. Ltd. Harbin Power Supply Company Harbin150010 China The Teaching Department Heilongjiang Open University Harbin150001 China

ISBN: (纸本)9798400709272

To solve the problem of insufficient and imbalanced data in Network Intrusion Detection (NID) in practical scenarios, which leads to low detection accuracy of the model. We propose a network intrusion algorithm based on the Federated Learning with Inheritance Private Model (FedPHP) , which applies Federation Learning (FL) to the field of NID to obtain a well-performing NID model in a privacy-preserving manner in scenarios with unbalanced small samples and multiple participants. This method uses oversampling to preprocess the data, and adds Inheritance Private Model (HPM), knowledge transfer and the matrix Adaptive motion estimation (Adam) algorithm in the training process. The experimental results show that each participant can still get a personalized model with high accuracy and strong robustness under the condition of providing limited data and not infringing the privacy of other parties. The average classification accuracy is 0.9874, and the performance is better than the traditional Deep Learning (DL) and FL network models. © 2023 ACM.

关键词： Learning systems

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Research on Semantic Object Measurement Algorithm based on Object Detection 10th

Research on Semantic Object Measurement Algorithm Based on O...

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10th international conference on Communications, signal Processing, and systems

作者： Wu, Wanqing Ma, Lin Wang, Bin Zhang, Zhongwang Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China China Natl Aeronaut Radio Elect Res Inst Shanghai 200233 Peoples R China

ISBN: (纸本)9789811903908;9789811903892

As a branch of machine vision, stereo vision has become a research hotspot in recent years. With the development of Augmented Reality (AR), image measurement technology has been proposed. We mainly study the algorithm of using RGB-D image to measure the size of objects in indoor environment, and comprehensively use computer vision technology and image target detection technology to realize the image measurement of objects. Aiming at the problem that there are many kinds of objects in the complex indoor environment and the measurement time in the actual environment, we propose a semantic target measurement algorithm based on target detection. Experimental results show that the target detection network we proposed can effectively identify the object category in indoor environment and measure the size of the recognized semantic target. At the same time. At the same time, the measurement time of the algorithm we proposed is very small, so the measurement efficiency can be improved.

关键词： Indoor Target detection 3D image image measurement

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Design of Cloud-Edge-End Collaborative Framework for Power Supply Intelligent Inspection Operation Scenario based on Unmanned Aerial Vehicle 8

Design of Cloud-Edge-End Collaborative Framework for Power S...

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8th IEEE Information technology and Mechatronics Engineering conference, ITOEC 2025

作者： Wang, Wei Qiu, Ping State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China Zhejiang International Studies University Hangzhou China

ISBN: (纸本)9798331517915

As a new type of tool with powerful perception, monitoring, and emergency response capabilities, unmanned aerial vehicles (UAVs) have the characteristics of flexibility, maneuverability, and high-altitude inspection. UAVs can greatly improve the efficiency of power facility maintenance and repairing, reduce personnel risks, and have gradually become an important auxiliary means for power supply operation and management. Currently, research on cloud-edge collaborative computing using UAVs has been applied to power systems, especially in their daily maintenance. However, there is not much discussion on building a systematic UAV-based cloud-edge collaboration system, especially to meet the requirements of power supply companies. Aiming at the problems such as the limited distance of image transmission, the lack of timeliness and visualization of image data processing in the application process of UAVs, in this paper, a "cloud-edge-end"collaborative framework has been proposed to achieve fast data return over the distance under signal coverage, and use the characteristics of local edge computing to quickly solve image preprocessing. © 2025 IEEE.

关键词： Drones

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