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image processing for automatic road inventory 15

Image processing for automatic road inventory

引用

15th International Scientific and Technical conference: Applied Mechanics and systems Dynamics, AMSD 2021

作者： Belim, S.V. Khiryanov, E.V. Kvashnina, P.A. Ostrinskaya, L.I. Omsk State Technical University 11 Mira ave Omsk644050 Russia Siberian State Automobile and Highway University Omsk Russia

Road image analysis is an important task for automatic road inventory. The determination geometric dimensions for the road and the identification road objects are subprocess of constructing a road digital image. In this article, two algorithms for solving different subtasks of automatic road image inventory are proposed. The first algorithm identifies road signs. A convolutional artificial neural network is used in this algorithm. The training set for the neural network is prepared. A computer experiment to determine the recognition effectiveness of road signs has been conducted. The second algorithm defines the edges of the pavement. The algorithm consists five stages. The edges of the road are modeled as straight lines. The result allows you to automatically determine the width of the road. © Published under licence by IOP Publishing Ltd.

关键词： Neural networks

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A Framework for Mining and Applying Alarm Rules Based on LLM

A Framework for Mining and Applying Alarm Rules Based on LLM

引用

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

作者： Guoqing Li Qian Zhang Haijun Su Mengyuan Zhu Mengqi Liu Haokai Zhang Development Center China Information Technology Designing & Consulting Institute Co. Ltd Beijing China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Driven by 5G technology, network performance and management requirements are becoming increasingly sophisticated. However, massive and complex alarm information is also coming, posing severe challenges to network operation and maintenance. Accurately identifying alarm correlations and locating alarm root causes has become a core problem for operation and maintenance. In response to this challenge, this paper proposes a framework for mining and applying alarm rules based on large language models, which applies the text understanding capabilities of large language models to alarm processing and can mine uncommon alarm rules. Experimental results show that this framework significantly improves the accuracy and coverage of alarm rules, and improves the response speed and efficiency of network operation and maintenance.

关键词： Technological innovation Correlation Large language models Knowledge based systems Signal processing algorithms Signal processing Real-time systems Maintenance Communication networks Optimization

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FPGA Based Neural Processor Design For image Detection of Traffic Signs

FPGA Based Neural Processor Design For Image Detection of Tr...

引用

International conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Mohammad Sakib Bin Selim Priom Ghosh Tuton Chandra Mallick Department of Electrical and Electronic Engineering Premier University Chittagong Chittagong Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

With the increasing complexity of traffic management systems, there is a critical need for efficient, high-performance solutions capable of processing visual data in real time to improve traffic control and safety. Traditional processors often face computational bottlenecks and latency issues when deploying neural networks for image recognition tasks. To address these limitations, this work proposes a dedicated neural processor implemented on the Cyclone iv FPGA, optimized for traffic signal image processing. The design leverages the parallelism and reconfigurability of FPGAs, enabling significant acceleration of computations while maintaining low power consumption. At its core, the processor integrates a neural network architecture with 4 layers tailored for detecting and classifying traffic signal colors, such as red, yellow, and green. This neural network is implemented using VHDL, ensuring efficient use of FPGA resources while achieving high computational performance. Key features of the design include real-time data processing, a focus on speed and accuracy, and a compact implementation that balances resource utilization. The effectiveness of the FPGA-based neural processor is evaluated using real-world traffic signal image datasets, demonstrating robust performance with 92% classification accuracy. Comprehensive evaluations of processing speed, classification accuracy, and resource utilization affirm the efficacy of the proposed neural network-based FPGA solution in addressing the complexities of real-time traffic signal processing and modern traffic management challenges.

关键词： Program processors Accuracy VHDL Scalability Neural networks Parallel processing Real-time systems Cyclones Resource management Field programmable gate arrays

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A Cooperative Gene Regulatory Network Target Entrapping in GNSS-denied Environments 12

A Cooperative Gene Regulatory Network Target Entrapping in G...

引用

12th International conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Shi, Ze Fan, Zhun Ma, Peili Hong, Juncao Li, Wenji Zhu, Guijie Huang, Huaxing Cai, Yuwei Dong, Zhaohui Zhu, Xiaomin Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shantou515063 China College of Systems Engineering National University of Defense Technology China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In recent years, the use of gene regulatory network (GRN) to guide swarm robots for target-entrapping tasks has received increasing attention. Even though many GRN-based algorithms for swarm aggregation morphogenesis generation are designed and verified in two-dimensional space, they are not suit-able for target trapping in three-dimensional space with GNSS-denied environments. Therefore, we propose a cooperative GRN (Co-GRN), which can guide swarm robots to complete target-entrapping tasks in GNSS-denied three-dimensional environments. The average distance and enveloping strength are analysed in several simulated scenes, and the effectiveness and flexibility of the proposed method are verified. © 2022 IEEE.

关键词： Genes

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Design and Development of an Intelligent Wireless Pond/Lake Cleaning Robot

Design and Development of an Intelligent Wireless Pond/Lake ...

引用

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

作者： Premkumar M Basavaraja Desai Bhoomika Mylar Chaithanya B K Monish G Department of Electrical and Electronics Engineering Dayananda Sagar College of Engineering Bangalore India Department of Electrical and Electronics Engineering Dayananda Sagar College of Engineering Bengaluru India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

This River pollution is a major environmental problem that has serious problems for human health, aquatic life, and the overall health of ecosystems. Floating garbage is a complicated problem to address, as it can be difficult to collect and remove from rivers and other waterways. Wireless river-cleaning robots can potentially be a valuable tool for addressing this problem. These robots can be equipped with a variety of sensors and actuators to detect and collect floating garbage, without the need for human efforts. This can help to reduce the cost and environmental impact of river cleaning operations, while also improving the effectiveness of these efforts. This paper aims to design and develop a wireless river-cleaning robot that is capable of autonomously detecting and collecting floating garbage. The robot will be designed with a variety of sensors, including cameras, ultrasonic sensors, and water quality sensors. image processing will be used to identify floating garbage, as well as to avoid obstacles and navigate the river safely. The robot will also be equipped with a collection system that can collect floating garbage and other debris. The robot will be controlled wirelessly using image processing and data will be stored on the website. This paper helps to reduce the water pollution on floating bodies and saves the lives of aquatic animals.

关键词： Wireless communication Wireless sensor networks image processing Surveillance Robot vision systems Cameras Cleaning Water pollution Rivers Sensors

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Optimization of image Compression and Decompression Performance Based on Genetic Algorithm

Optimization of Image Compression and Decompression Performa...

引用

Interactive Intelligent systems and Techniques (IIST), International conference on

作者： Weishi Li Chun Yin Fujian Polytechnic of Information Technology Fuzhou China

ISBN: (数字)9798350374421

ISBN: (纸本)9798350374438

As one of the main carriers of information transmission and storage, image data need to be compressed in some application scenarios. Traditional image compression methods, although reducing the size of image files to a large extent, are still difficult to meet the performance requirements of massive, high-speed image data compression processing. In this paper, an image compression and decompression performance amelioration method based on genetic algorithm is proposed, aiming to ameliorate the efficiency and quality of image compression, while reducing the time and computational complexity of decompression. This paper first introduces the background and current status of image compression technology, and analyzes the progress of existing image compression research. Then, the basic principles of genetic algorithm and its application in image compression are introduced in detail. On this basis, an image compression algorithm based on genetic algorithm is proposed. The algorithm ameliorates the parameter settings in the image compression process through genetic algorithm to achieve efficient compression of images. At the same time, combined with the image decompression process, the decompression algorithm is ameliorated to reduce the consumption of computational resources. The research in this paper provides new ideas and methods for the performance amelioration of image compression and decompression technology, which has important theoretical and practical value.

关键词： image quality image coding Data compression Information processing image storage Intelligent systems Computational complexity Optimization Genetic algorithms Convergence

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AI-based Gender Identification using Facial Features 2

AI-based Gender Identification using Facial Features

引用

2nd International conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Venkateswarlu, B. Sunanda, N. Kumar, A Yaswanth Mani Reddy, A Naga Siva Charan Hyndav, B. Ram Gopal Koneru Lakshmaiah Education Foundation Department of Cse Guntur India Vnr Vignana Jyothi Institute of Engineering & Technology Department of CSE-DS CYS Ai&ds Hyderabad India

ISBN: (纸本)9781665456302

The identification of gender through facial images has gained significant importance in recent times. In the domain of human recognition through image processing, biometric features like facial structure, iris, voice, and other physical characteristics are employed to establish unique identification of individuals. As the quantity of image data on the internet continues to rise, the need for Artificial Intelligence (AI) algorithms capable of performing classification tasks like face and gender recognition has grown exponentially. With the increasing prevalence of machine-to-machine interaction, and the emphasis on ethical, efficient, and individual-centric systems becoming more pronounced, gender identification software has become an increasingly popular tool. Automated gender identification software has gained popularity in the era of exchange of information between two or more machines. The method for detecting the face involves using the Haar Cascade face detection technique, which employs the Viola-Jones face recognition algorithm. After detecting the face, the eyes, mouth, and nose can be identified as facial features using various computer vision techniques. Adaptive filters are utilized to decrease noise prior to detection process in order to increase gender identification accuracy. The gathered facial characteristics serve the neural network as input or test data, which is then trained on these features. The neural network is designed to learn gender-specific features and operate as a classifier for gender detection. © 2023 IEEE.

关键词： image enhancement

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Study on Prostate image Segmentation Using Improved U-NET

Study on Prostate Image Segmentation Using Improved U-NET

引用

International conference on image, Vision and Intelligent systems, ICivIS 2021

作者： Sun, Mengya Department of Mathematical Sciences University of Electronic Science and Technology of China Chengdu610000 China

ISBN: (纸本)9789811669620

Medical image plays a key role in the analysis and treatment of the patient’s condition in today’s medicine, but the clinical processing of medical image is still largely dependent on the subjective experience of doctors, and the process of manual extraction of information is time-consuming and labor-consuming. With the development of deep learning, it has become a trend to apply deep learning to medical image processing. However, due to the characteristics of medical images, many advanced segmentation algorithms fail to achieve good results in medical images. In addition, medical image data generally have the problem of insufficient or too small data sets, and there are few ways to obtain samples, mainly from some hospitals and medical institutions. Besides, the labeling work is not competent for ordinary people. So how to realize the automatic analysis and processing of medical images has always been a hot topic in the field of computer science. In this paper, we designed an experiment of prostate segmentation algorithm, built a model using a U-Net network that performs well in the field of medical image segmentation, and tried to improve its performance by combining a variety of structures, such as ASPP, ResNext, and attention module. The results show that our proposed model achieves better segmentation performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Realtime Scene Enhancement of Low Light image/Video

Realtime Scene Enhancement of Low Light Image/Video

引用

Intelligent Computing and Human-Computer Interaction (ICHCI), International conference on

作者： Huixia Zeng Ping Xiao School of Electronic & Information Engineering Guangzhou City University of Technology Guangzhou Guangdong China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

The quality of image and videos plays a vital role in case of real-time systems. images are captured without sufficient illumination, lead to low dynamic range and high propensity for generating high noise levels. The processing ideas and networks of several mainstream low-light image enhancement neural network algorithms such as DRBN, DSLR, ZeroDCE, EnlightenGAN, as well as the key technologies used are compared and analyzed. In this paper, the EnglightenGAN neural network is used as the core image enhancement algorithm, and NVIDIA AGX Xavier equipment and video stream processing technology are used to achieve the entire process of video acquisition, video frame encoding, image enhancement processing, frame decoding, and video stream display. It can adapt to most low-light and dark scenes which has a good enhancement effect on video streams collected in low-light scenes. The real-time performance and image enhancement effect of the system achieved good results, with FPS reaching 10, SSIM reaching 0.51753, and PSNR reaching 27.89973.

关键词： Runtime Neural networks Lighting Streaming media Real-time systems Stability analysis image enhancement Thermal stability Videos Noise level

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Comparison of image Feature Detection algorithms 9

Comparison of Image Feature Detection Algorithms

引用

9th International conference on Dependable systems and Their Applications, DSA 2022

作者： Xu, Fan Liu, Xia Cui, Yanli Yan, Mingdie Lai, Zhongyuan Intelligent Manufacturing of JianghanUniversity Hubei Wuhan China Artificial Intelligence of JianghanUniversity Hubei Wuhan China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

The key of image processing is to extract feature points and feature vectors by appropriate methods. In order to analyze the application effect of common feature extraction methods in different scenarios, this paper adopts SIFT, BRISK, ORB, KAZE and AKAZE methods to rotate, adjust brightness, blur and zoom images based on two data sets. The operating efficiency, the ratio of feature points before and after operation, the best matching rate and accuracy rate of feature points pair of the five methods are analyzed, and the suggestions for selecting reasonable feature detection methods for different application scenarios are given. © 2022 IEEE.

关键词： Feature extraction

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