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检索条件"任意字段=Image Processing: Algorithms and Systems XI"

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A GPU Parallel Stitching Algorithm for Low-Altitude Parallax Scenes

A GPU Parallel Stitching Algorithm for Low-Altitude Parallax...

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Machine Learning and Computer Application (ICMLCA), International Conference on

作者： Sha Liu Shan Zhou Yan Hu China Electronics Technology Group Corp 54th Research Institute Shijiazhuang China China Electronics Technology Group Corp 20th Research Institute Xi'an China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

Panorama stitching of low-altitude captured images is a very interesting and challenging task. Since 3D scenes are not planar, there may be significant changes in the relative positions of scene structures in each perspective in the images, thus causing parallax. Existing image stitching algorithms often fail in parallax scenes and are not efficient enough to meet the real-time processing requirements of drones. To address this problem, we introduce gridded dense projection estimation and design a parallel computing unit suitable for GPUs for rapid modeling of projection fields. Experimental results show that our algorithm significantly outperforms the comparison algorithms in terms of computational performance, has high real-time performance, and achieves nearly the same level of stitching quality and naturalness.

关键词： Three-dimensional displays Computational modeling Graphics processing units Machine learning Parallel processing Real-time systems Partitioning algorithms Computational efficiency image stitching Periodic structures

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Research on Parallel Computation and Memory Optimization of Multi-Core Embedded DSP Technology in High Precision Missile Terminal Guidance image Tracking Algorithm

Research on Parallel Computation and Memory Optimization of ...

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IEEE International Conference on Power, Intelligent Computing and systems (ICPICS)

作者： Fuming xia Bo Jing Mingge Huo xiaoxuan Jiao Shenglong Wang Jinxin Pan School of Aeronautical Engineering Air Force Engineering University Xi'an China Beijing Electro-Mechanical Engineering Institute Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

This paper mainly discusses how to effectively use multi-core embedded digital signal processor (DSP) technology to realize parallel computation of image tracking algorithm and memory optimization in high precision missile terminal guidance system. With the increasing requirement of missile strike accuracy in modern war, the vision-based terminal guidance technology has been widely concerned because of its strong autonomy and anti-jamming ability. However, such image processing algorithms are usually highly computation-intensive and data dependent, which requires high real-time and hardware resources. In this paper, the key steps of image tracking algorithm are analyzed and reconstructed deeply, and the architecture suitable for parallel processing of multi-core DSP is designed by modularization. The parallel computing advantages of multi-core DSP are given full play through task division and scheduling strategies to improve the execution efficiency and real-time performance of the algorithm. Secondly, the research also focuses on memory optimization, including the design of data cache strategy, reducing data redundancy, improving memory access locality, etc., and strives to maximize the data reading speed and reduce the memory bandwidth pressure under the condition of limited embedded system resources. To sum up, this research is committed to building an efficient and stable multi-core embedded DSP environment, parallel computing model and memory optimization scheme for high-precision missile terminal guidance image tracking algorithm, which has important theoretical value and practical significance for improving the intelligence level and combat efficiency of missile terminal guidance system.

关键词： Missiles Processor scheduling Digital signal processors Redundancy Signal processing algorithms Parallel processing Real-time systems Scheduling Optimization image reconstruction

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Scanning Electron Microscope image Data Acquisition and Transmission System

Scanning Electron Microscope Image Data Acquisition and Tran...

引用

Intelligent Control, Measurement and Signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Yang Zhang Zuye Wang Yong Zhao School of Instrument Science and Opto-Electronics Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Scanning electron microscopy (SEM) has emerged as a pivotal tool in scientific research and engineering, offering unparalleled insights into the micro- and nano-scale world. However, traditional SEM systems often entail bulky setups and complex data acquisition processes, limiting their accessibility and efficiency in various applications. This paper designs a desktop SEM image data acquisition and transmission system based on FPGA. The system is divided into two parts: signal modulation and AD sampling circuit and image data processing and transmission circuit. By designing a circuit to process SE/BSE signals, signal modulation and analog-to-digital conversion are achieved. The sampling data is processed using the internal resources of the FPGA and transmitted to the PC through the USB3.0 high-speed interface, and finally displayed as a grayscale image. The system realizes real-time collection, processing and transmission of SEM signals, providing convenience and efficiency for scientific research and engineering applications. Experimental verification shows that the system performs well and has certain application potential.

关键词： Scanning electron microscopy Machine learning algorithms Microscopy Data acquisition Modulation Gray-scale Data processing Universal Serial Bus Real-time systems Field programmable gate arrays

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BUILDING STRUCTURE RECONSTRUCTION WITH UAV-BORNE THROUGH-WALL RADAR

BUILDING STRUCTURE RECONSTRUCTION WITH UAV-BORNE THROUGH-WAL...

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IET International Radar Conference 2023, IRC 2023

作者： Zhang, xi Zhong, Shichao Yang, xiaopeng Tang, Liangyong 濦chool of Information and Electronics Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China Novasky Technology Company Limited by Shares Changsha China

ISBN: (纸本)9781839539954

Through-wall radar (TWR) building structure reconstruction holds significant practical significance and research value in various fields, such as counter-terrorism operations, disaster relief, and surveillance. However, traditional TWR is a hand-held or vehicle-mounted equipment, which is challenging to use in tower blocks and complex terrain scenes. To solve the limitations of traditional TWR systems, this paper utilizes a TWR system equipped on a small multi-rotor unmanned aerial vehicle (UAV) to perform synthetic aperture radar (SAR) mode scans and reconstruct building structures. First, this paper reviewed back-projection (BP) and range-Doppler (RD) algorithms for building structure reconstruction. Then, UAV-borne TWR was used to efficiently collect radar echoes from the front and side of building. Finally, BP and RD algorithms were employed to image wall layout with high-quality. The experiments show that UAV-borne TWR offers higher efficiency and convenience in complex scenes. The performed experiment further extends the conventional imaging scenarios. This paper demonstrates that building structure reconstruction with UAV-borne TWR is a feasible approach and has wild applications. © The Institution of Engineering & Technology 2023.

关键词： Synthetic aperture radar

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FWAVs Object Detection Based on YOLOv8 In Adverse Weather

FWAVs Object Detection Based on YOLOv8 In Adverse Weather

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image processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Junying Li Chenguang Zhai xiaojun Yang Fu Qiang School of Aeronautics Northwestern Polytechnical University Xi’an China National Key Laboratory of Aircraft Configuration Design

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In recent years, Flapping-wing Micro Aerial Vehicles (FMAVs) have gained increasing attention due to their biomimetic appearance and excellent maneuverability. Equipping them with object detection systems to make them more intelligent is becoming a major trend. This paper proposes an aerial photography small object detection algorithm under adverse weather conditions based on YOLOv8. Using a custom dataset, the concluding mAP50 (mean Average Precision at Intersection over Union threshold of 0.5) reached 0.761, surpassing other datasets. The mAP50 improved from 0.627 to 0.708 compared to model validation on the VisDrone2019 dataset, surpassing other mainstream object detection models.

关键词： Computer vision Machine learning algorithms Snow image processing Biomimetics Object detection Machine learning Market research Satellite images Meteorology

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Lightweight scale-focused pyramid network for remote sensing image object detection

Lightweight scale-focused pyramid network for remote sensing...

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Natural Language processing (ICNLP), International Conference on

作者： Daxiang Li Zhan Ji Ying Liu Yao Tang School of Communication and Information Engineering Xi'an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Lightweight target detection algorithms are crucial for real-time processing of remote sensing satellite images. However, due to the large size of remote sensing images and high computational costs, there are difficulties in dealing with significant changes in target scales. Existing lightweight models tend to prioritize detection speed at the expense of detection accuracy. To address this issue, we propose a lightweight scale-focused pyramid network(LSFPN). Firstly, we design a lightweight BaseNeck module to reconstruct the network's neck part, eliminating complex feature fusion processes and reducing network complexity. Additionally, we design a multi-scale expansion(MSE) module to optimize multi-scale feature extraction and improve the network's multi-scale target detection performance. Lastly, considering the significant scale variations in remote sensing images, we adopt the K-means++ clustering algorithm to re-determine the initial anchor box sizes, further enhancing the accuracy of target detection. Experimental results on the HRSC2016 dataset demonstrate that our method achieves optimal detection accuracy with reduced computational and parameter costs of 37.70% and 59.42% respectively, while improving accuracy by 0.81% with less model complexity.

关键词： Accuracy Computational modeling Object detection Real-time systems Natural language processing Complexity theory Satellite images Neck Remote sensing image reconstruction

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Data Dimension Reduction makes ML algorithms efficient

Data Dimension Reduction makes ML Algorithms efficient

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2022 International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2022

作者： Khan, Wisal Turab, Muhammad Ahmad, Waqas Ahmad, Syed Hasnat Kumar, Kelash Luo, Bin Anhui University School of Computer and Technology Hefei230039 China Mehran Uni. of Enginerring & Technology Dept: Computer Systems Engineering Hyderabad Pakistan Northwestern Polytechnical University School of Aeronautics Shaanxi Xi'an710129 China Mehran Uni. of Enginerring & Technology Department Electrical Engineering Hyderabad Pakistan

ISBN: (纸本)9781665490870

Data dimension reduction (DDR) is all about mapping data from high dimensions to low dimensions, various techniques of DDR are being used for image dimension reduction like Random Projections, Principal Component Analysis (PCA), the Variance approach, LSA-Transform, the Combined and Direct approaches, and the New Random Approach. Auto-encoders (AE) are used to learn end-to-end mapping. In this paper, we demonstrate that pre-processing not only speeds up the algorithms but also improves accuracy in both supervised and unsupervised learning. In pre-processing of DDR, first PCA based DDR is used for supervised learning, then we explore AE based DDR for unsupervised learning. In PCA based DDR, we first compare supervised learning algorithms accuracy and time before and after applying PCA. Similarly, in AE based DDR, we compare unsupervised learning algorithm accuracy and time before and after AE representation learning. Supervised learning algorithms including support-vector machines (SVM), Decision Tree with GINI index, Decision Tree with entropy and Stochastic Gradient Descent classifier (SGDC) and unsupervised learning algorithm including K-means clustering, are used for classification purpose. We used two datasets MNIST and FashionMNIST Our experiment shows that there is massive improvement in accuracy and time reduction after pre-processing in both supervised and unsupervised learning. © 2022 IEEE.

关键词： Principal component analysis

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Real-time image Enhancement for Emergency Rescue Scenarios in Smart Grids

Real-time Image Enhancement for Emergency Rescue Scenarios i...

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Electronic Technology, Communication and Information (ICETCI), IEEE International Conference on

作者： Zeng Dou Jingyi Tao Ning Lv Li Cong Chengbin Huang Tao Su State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun China School of Electronic Engineering Xidian Hangzhou Institute of Technology Xidian University Hangzhou China School of Electronic Engineering Xidian University Xi’an China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

image enhancement technology plays an important role in the practical application of detecting underwater tunnels. By improving image quality, it enhances the accuracy and reliability of detection results. However, in deep-sea scenes, the problem of image color distortion and detail loss is common. To address this issue, a real-time underwater image enhancement algorithm based on weighted multi-branch correction has been proposed in this paper. The algorithm uses a combination of multi-scale and multi-branch techniques to process the image. The multi-branch correction module has been improved by increasing the weighting and downsampling strategy, which not only improves the processing speed but also ensures the processing effect. In the air-frequency domain interactive processing module, zero padding and window functions have been introduced to reduce the ringing effect and block effect. In addition, the smart grid system provides stable power support for underwater tunnel search and rescue, which ensures the safety of the search and rescue environment and also provides a good experimental lighting environment for the image enhancement algorithm. At the same time, the model has been trained and validated using self-constructed real deep-sea datasets. The existing algorithms and the algorithm proposed in this paper have been compared and analyzed comprehensively. The experimental results show that the algorithm proposed in this paper is better than the existing algorithms in terms of processing effect, and the processing speed is improved by about 12.7%.

关键词： image quality image color analysis Lighting Information filters Real-time systems Smart grids Safety

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Autofocus optical imaging system based on image processing 14

Autofocus optical imaging system based on image processing

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14th International Conference on Information Optics and Photonics, CIOP 2023

作者： Teng, Teng Chen, Guan xiaofei, Wang Hua, Fan Dongjie, Ju Zhaojuan, Chi Jinan250000 China Shandong JINGWEI Intelligent Equipment Co. Ltd Weifang261000 China

ISBN: (纸本)9781510671744

Autofocus optical imaging systems are widely used in industrial inspection, medical diagnosis, drone photography, machine vision and other fields. image-based autofocus algorithms typically consist of two key steps: image sharpness evaluation and search strategy. In this study, we propose an advanced image-based autofocus algorithm specifically designed for industrial image measurement. In order to improve the accuracy of the clarity assessment, we improve the existing method by introducing Gaussian filtering and threshold controlled Laplace operators. This improvement effectively reduces the effects of noise and light intensity changes, improving the reliability of clarity measurements. In addition, we propose a novel distance adjustment strategy combining coarse and fine tuning as part of the search strategy. This strategy reduces the interference of local peaks, allowing the algorithm to accurately identify the best image focus. The proposed image-based autofocus algorithm has several advantages, e.g. high focusing accuracy, high repeatability, and stability under light intensity changes2. These advantages make it ideal for industrial image measurement applications. To verify the effectiveness of our method, we build an experiment setup in a lab using ADLINK PCI-9114-DG board and a custom-designed lens. The results show that the sensitivity and performance of the autofocus system meet certain requirements. In summary, the image-based autofocus algorithm that we developed enables accurate and reliable focusing. It overcomes some challenges such as noise and light intensity changes to ensures optimal image focus, and improves overall image quality. The successful implementation of our autofocus system can benefit industrial inspection, medical diagnostics and many other applications. © 2023 SPIE.

关键词： Diagnosis

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VIBRATION-BASED BRIDGE DAMAGE DETECTION USING image-BASED PRE-TRAINED DEEP LEARNING NETWORK

VIBRATION-BASED BRIDGE DAMAGE DETECTION USING IMAGE-BASED PR...

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15th ASME Conference on Smart Materials, Adaptive Structures and Intelligent systems (SMASIS)

作者： Song, xi Dyogi, Joshua Cho, Chunhee Univ Hawaii Manoa Honolulu HI 96822 USA

ISBN: (纸本)9780791886274

Vibration-based damage detection has become one of the principal practices to prevent structural collapses in civil, mechanical, and other engineering disciplines. Meanwhile, with the advancement of computing technology, various machine learning (ML) approaches have been applied toward structural damage detection through the application of post-processing algorithms. To accurately predict damages with ML, large amounts of structural response data are collected from a series of sensors attached to the structure. Therefore, the damage diagnosis requires high computational efforts. To address such an issue, this paper presents a revolutionary approach utilizing an image-based pre-trained convolutional neural network (CNN) to detect bridge damage locations and severities. Our research adopted scalograms from wavelet transform to convert structure acceleration data into image data. Compared with the traditional frequency analysis derived from the Fourier transform, the new method maintains both spatial and temporal information from the original structural behaviors. To generate CNN learning features, six channels of acceleration data are gathered from six strategically selected points of a finite element (FE) bridge model. Two pre-trained CNN, AlexNet and Resnet, are selected to conduct transfer machine learning for higher training efficiency. The performances of the proposed method are assessed with various damage scenarios. The prediction accuracies of AlexNet and Resnet are 98% and 100%, respectively.

关键词： Structural Damage Detection Machine Learning Deep Learning Transfer Learning Wavelet Transform Scalogram

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