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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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A Low-Cost Deployment Case Study for Industrial IoT Data Collection and processing

A Low-Cost Deployment Case Study for Industrial IoT Data Col...

引用

Intelligent Technology and Embedded systems (ICITES), International conference on

作者： Jiajun Zhang Biao Hu College of Engineering China Agricultural University Beijing China

ISBN: (数字)9798331506292

ISBN: (纸本)9798331506308

Despite the widespread implementation of SCADA systems in factories for centralized data management, their functionality is restricted to devices equipped with sensors. Manual readings are still prevalent for critical instruments like temperature and pressure gauges, lacking interfaces, leading to inefficiencies and errors. Upgrading production line sensors with IoT-enabled instruments is costly and disruptive. To tackle this, we propose a cost-effective data acquisition system that leverages edge acquisition devices and deep learning algorithms without altering the existing equipment and processes. Our solution involves installing new sensors or cameras in critical areas and employing image recognition algorithms alongside indirect measurement techniques. This approach enhances the real-time accuracy of data collection, significantly boosts the overall efficiency of the production process, and reduces operating costs.

关键词： Temperature sensors Temperature measurement Deep learning Accuracy image edge detection Data acquisition Data collection Production facilities Sensors Edge computing

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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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Raindrop Removal for In-Vehicle Camera images with Generative Adversarial Network

Raindrop Removal for In-Vehicle Camera Images with Generativ...

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2022 IEEE International conference on systems, Man, and Cybernetics, SMC 2022

作者： Zhao, Zihao Jiang, Min Guo, Jia Yang, xiaoyu Hu, Yudie Zhou, xianlong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Raindrop removal for in-vehicle camera images is useful for surveillance and analysis system such as intelligent driving or case diagnosis. As in rainy days, images taken by in-vehicle cameras such as monitor or drive recorder often suffer from noticeable image degradation, and make it difficult to identify the objects on the road. With the uncertainty of raindrop distribution and the complexity of raindrop status, the attached raindrops of in-vehicle camera images can have different effects. And the large-diameter raindrop will greatly reduce the image quality. However, most of the popular deraining algorithms have been successful in recovering images with small raindrop noise and tiny image distortion, but fails to restore those with raindrops covering large areas. We propose a single image raindrop removal network based on Generative Adversarial Network for in-vehicle camera images. We incorporate two overlapping attention layers into the deraining network, which adopt task-driven visual attention and content perception mechanism. The former obtains features by recursive neural network to guide network more interested in the raindrop regions and the surrounding structures, while the content perception schema extracts the features far from raindrops. Moreover, we collect a new benchmark for In-Vehicle Camera image Deraining (IVCRID), it chooses in-vehicle camera images from various deraining dataset. The experimental results show that the proposed deraining network outperforms other state-of-the-art raindrop removal methods in image recovering test on the IVCRID dataset and also has the best performance on the experiments of traffic objects detection. © 2022 IEEE.

关键词： Generative adversarial networks

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image Encryption Based On Chaotic Scrambling And DNA Sequence Multi-rule Coding

Image Encryption Based On Chaotic Scrambling And DNA Sequenc...

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6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Yunyu Chen Yuan Zhang College of Big Data and Artificial Intelligence China Telecom Research Institute Shanghai China College of Information Science and Electronic Engineering Zhejiang University College of Big Data and Artificial Intelligence China Telecom Research Institute Hangzhou China

An image encryption algorithm based on chaotic scrambling and DNA sequence multi-rule coding is proposed. Through the research of related algorithms, the original image and the chaotic sequence generated by Logistic chaos are encoded, fused and decoded by using the multi-rule coding principle of DNA sequence, and the pixel value is changed to encrypt. Then, the PWLCM algorithm is used to generate new chaos to scramble the pixel blocks of the image, so as to obtain the encrypted image. The experimental results show that the algorithm not only improves the encryption efficiency of the traditional encryption algorithm, increases the key space, but also resists the exhaustive and statistical attacks.

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Two-step Method Grid Map Merging Based on Corner Points 41

Two-step Method Grid Map Merging Based on Corner Points

引用

第41届中国控制会议

作者： Chong Chai YunZhi Huang Hefei University of Technology School of Electrical and Automation Engineering

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Multi-robot SLAM(Simultaneous Localization and Mapping) can be used in many collaboration-based operations,such as military rescue *** merging is the most obvious solution to the multi-robot ***,traditional corner-based map fusion algorithms failed in complex *** paper proposes a corner-based two-step method for map merging in complex ***,the map is preprocessed,including orthogonal rotation,filtering,and edge *** corners are identified by Harris Corner detection *** constructing a convex quadrilateral based on the corner points, the overlapping area can be selected for the first ***,the intersection of the obtained quadrilateral diagonals is used as a new feature point,and a triangle is constructed for matching to obtain the final overlapping ***,the rotation angle and displacement can be obtained by calculating the transformation *** results show that this method can solve the complex grid map merging problem effectively.

关键词： Multi-robot SLAM Map merging image processing Two-step method

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Rendering Extreme Weather Phenomena Using Generative AI Base on ControlNet

Rendering Extreme Weather Phenomena Using Generative AI Base...

引用

International conference on Vision, image and Signal processing (ICVISP)

作者： Chenchen He xia Yuan Yi Zheng xi Wu Jing Hu Ting Wang School of Computer Science Chengdu University of Information Technology Chengdu China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Generative artificial intelligence (AI) has rapidly advanced in recent years, achieving significant breakthroughs in computer vision and image processing. However, there are still many challenges in rendering complex scenes, particularly generating and controlling images for extreme weather conditions. In this work, we propose a novel generative model based on ControlNet and achieve precise control and high-quality rendering of extreme weather features by combining a sparsely connected Squeeze-and-Excitation module with a zero-convolution design. The proposed model excels in generating highly realistic extreme weather scenes, including heavy rain and snow, while significantly enhancing the ability to fine-tune weather characteristic parameters. Experimental results validate the model's exceptional generation quality and robust control stability, which not only realistically reproduces complex weather scenes but also allows flexible adjustments to weather characteristics such as rainfall intensity, snowflake density, and flame range. Compared to previous methods, the model demonstrates substantial improvements in image detail fidelity, weather feature consistency and generation efficiency. This technology shows immense potential for applications in disaster simulation and early warning systems.

关键词： Computer vision Rain Generative AI Computational modeling Disasters image processing Alarm systems Rendering (computer graphics) Stability analysis Meteorology

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CSIFA: A Configurable SRAM-based In-Memory FFT Accelerator

CSIFA: A Configurable SRAM-based In-Memory FFT Accelerator

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International conference on Application Specific systems (ASAP), Architectures and Processors

作者： Yiyang Lin Yi Zou Yanfeng Yang School of Microelectronic South China University of Technology Guangzhou China

ISBN: (数字)9798350349634

ISBN: (纸本)9798350349641

In the era of Artificial Intelligent (AI) and big data, the demand for signal processing algorithms for large datasets has grown across various fields, notably in the application of the Fast Fourier Transform (FFT). This algorithm is crucial in computation and storage-heavy applications like image and radar signal processing, where it has been integral for decades. In-memory computing (IMC), unlike traditional computing models, integrates computation and storage in the same unit, reducing data transfer time and energy costs. This paper presents CSIFA, a hardware accelerator for up to 1024-point FFT algorithm, utilizing SRAM and some other digital logic to enhance efficiency. Our evaluation indicates that CSIFA performing FFT is able to achieve an overall throughput 15MB/s, computational power 6.65mW, and 115.3GOPS, showcasing its high throughput and energy efficiency for edge application scenarios.

关键词： Program processors Fast Fourier transforms Signal processing algorithms systems architecture Random access memory Throughput Energy efficiency

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An image processing Application: Design of VLSI based Bilateral Filter using Distance Matrix Method 2

An Image Processing Application: Design of VLSI based Bilate...

引用

2nd IEEE International conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Saranya, C. Vijayananth, M.V. Mouleeshwaran, S. Kaviyarasu, D. K.S.Rangasamy College of Technology Department of ECE Tiruchengode India

ISBN: (纸本)9781665493963

The entire design structure is being impacted by advancements in electronic technology, which is posing a number of challenges for digital systems. In the fields of communications, image processing, and multimedia, VLSI-based image filter architectures are essential. Numerous applications based on VLSI architecture experience problems with significant size components that result in a filter design error during the floating-point arithmetic stages. The increased complexity of the filter design's component list is the issue encountered in the VLSI architecture. Noise removal often involves using the image filtering technique. There is a greater need for performance-based hardware sorting algorithms for low power embedded real-Time image filtering applications. Noise often taints an image while being transmitted from one location to another. The purpose of the denoising technique is to reduce noise while keeping any potential signal features that may be significant. Compare to the prior method, the usage of multipliers can be reduced based on the techniques of distance matrix and hardware resource sharing. The speed of the proposed VLSI architecture for the bilateral filter can be faster than the existing approach. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Autonomous Drone for Surveying Flooded Area and Performing Optimal Rescue Operation Using image processing

Autonomous Drone for Surveying Flooded Area and Performing O...

引用

conference on Modeling of systems Circuits and Devices (MOS-AK India)

作者： L David William Raj S Sampreeth R Sri Lakshmi Leena Juliet S Sneha Department of ECE MVJ College of Engineering Bangalore India

ISBN: (数字)9798350395556

ISBN: (纸本)9798350395563

During life threatening conditions caused due to natural disasters, search and rescue operations manually has not been possible to implement quickly. To overcome this issue, the proposed methodology provides a solution of using an autonomous drone with the help of image processing techniques in flooded scenarios. When any victims are identified, our proposed system aids in the rescue operations by visually screening the flooded areas and sending a signal to the rescue team. Using this UAV camera several image processing algorithms are performed to detect survivors to approximate the area of the flooded region.

关键词： Technological innovation image segmentation Disasters Autonomous aerial vehicles Cameras Approximation algorithms Classification algorithms

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Real-Time Car Parking Management System

Real-Time Car Parking Management System

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Intelligent algorithms for Computational Intelligence systems (IACIS), International conference on

作者： Ramachandra A C H S Jagadeesh Sanjay J C Vinay D H Siddesh H K Department of EEE Nitte Meenakshi Institute of Technology Bengaluru India Department of ECE Atria Institute of Technology Bengaluru India Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Urban areas in countries like India face significant challenges with traffic congestion and parking management, leading to increased fuel consumption, air pollution, and inefficiencies in transportation. We propose Real-Time Car Parking Management System (RTCPMS) to address the issues by developing a smart parking system that leverages image processing with cameras and utilizes microcontrollers for wireless control of peripherals such as gates, indicating LEDs, and LCD displays. The work integrates image processing techniques to monitor parking space occupancy using cameras installed at parking locations. The microcontroller, equipped with Wi-Fi capabilities, is employed to wirelessly control various peripherals within the parking system. Upon approaching a parking location, users receive real-time information on parking availability via LCD displays, while gate access and indication of available spaces are facilitated through LEDs. The implementation of the system accurately detects parking space occupancy, providing users with up-to-date information on available parking spots. By providing real-time information to users and enabling wireless control of parking peripherals, the system contributes to reducing congestion in parking area, minimizing fuel consumption, and promoting environmental sustainability in urban areas. The results obtained during the process are better compared to the existing.

关键词： Wireless communication Microcontrollers image processing Urban areas Aerospace electronics Logic gates Light emitting diodes Real-time systems Liquid crystal displays Fuels

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