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

共 16439 条记录，以下是1681-1690 订阅

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KAS-YOLOv5: A Novel Target Detection Algorithm for Aerial images

KAS-YOLOv5: A Novel Target Detection Algorithm for Aerial Im...

引用

Chinese Control and Decision conference, CCDC

作者： Yuning Wei Yang Liu Boyu Li Sirui Liu Yang Xu Yingyu Zhang School of Automation Shenyang Aerospace University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Target detection in aerial image is a challenging task due to the high proportion of small targets, severe occlusion, and large scale variations. In this paper, a novel KAS-YOLOv5 algorithm is proposed based on YOLOv5. First, the K-Means++ clustering algorithm is used to regenerate the initial anchor boxes suitable for the target size of the aerial image. Secondly, Adaptively Spatial Feature Fusion (ASFF) is used to solve the inconsistency problem between features of different scales in aerial images. Finally, a dynamic label assignment strategy Simplified Optimal Transport Assignment (SimOTA) is used to transform the label assignment into an optimal transmission problem, thereby improving the severe occlusion phenomenon. The experimental results demonstrate a high accuracy rate of 42.2% on the VisDrone2019 dataset, outperforming other algorithms.

关键词： YOLO Training Knowledge engineering Accuracy Heuristic algorithms Clustering algorithms Graphics processing units

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A Review on the Applications of Machine Learning and Deep Learning algorithms for image Recognition

A Review on the Applications of Machine Learning and Deep Le...

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Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： Ketan Wanjari Prateek Verma Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition.

关键词： Deep learning Ethics Data privacy image recognition Machine learning algorithms Reviews Social networking (online) Vectors Security Stakeholders

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Abnormal Behavior Detection in Electronic-Exam Videos Using BeatGAN 8

Abnormal Behavior Detection in Electronic-Exam Videos Using ...

引用

8th International Iranian conference on Signal processing and Intelligent systems, ICSPIS 2022

作者： Atabay, Habibollah Agh Hassanpour, Hamid Shahrood University of Technology Image Processing & Data Mining Lab Shahrood Iran

ISBN: (纸本)9781665476232

Abnormal Behavior Detection is a core ability to recognize cheating in electronic exams (e-exams), especially in the scenes where a fraudulent candidate hides unauthorized resources from the view of the proctor. This work utilizes the BeatGAN algorithm to detect behavioral abnormalities in the skeleton-based motion information of candidates during remote e-exams. We examine the algorithm on the previously published dataset of skeleton features extracted from e-exam videos. The results show that BeatGAN outperforms previously investigated algorithms in sequence anomaly detection using the raw joint-coordinate, implicit bone-length-and-angle and explicit bone-length feature types. But, in event anomaly detection, BeatGAN does not improve the state-of-the-art results on the dataset. © 2022 IEEE.

关键词： Anomaly detection

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Multi-factor machining condition monitoring method based on ordinal pattern analysis and image matching

Multi-factor machining condition monitoring method based on ...

引用

2022 Global Reliability and Prognostics and Health Management conference, PHM-Yantai 2022

作者： Li, Yazhou Dai, Wei Li, Tong Beihang University School of Reliability and Systems Engineering Beijing China Xi'an Modern Control Technology Research Institute Xi'an China

ISBN: (数字)9781665496315

ISBN: (纸本)9781665496315

The existing machining process condition monitoring methods usually only monitor the single anomaly, ignoring the multi-factor coupling anomaly in the actual complex machining process. Aiming at three kinds of typical anomalies frequently occurring in cutting, a new multi-factor coupling machining condition monitoring method based on ordinal pattern (OP) analysis and image matching is proposed. Firstly, the OP analysis model is developed to transform the condition monitoring signal into a gray image based on multi-parameter ordinal pattern spectrum (OPS), which optimizes the parameter selection process. Then, an OPS image dictionary template set of different condition monitoring signals is established. A condition recognition method based on OPS image matching is proposed to identify the sample processing state. Finally, a cutting experiment with 8 machining states is designed to verify the effectiveness of the method. The results show that the proposed method can accurately identify various cutting anomalies in different machining environments. © 2022 IEEE.

关键词： Condition monitoring

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An Accurate and Time-Efficient Subtractor by Cross Format Coding in Stochastic Computing

An Accurate and Time-Efficient Subtractor by Cross Format Co...

引用

IEEE Asia Pacific conference on Circuits and systems (APCCAS)

作者： Zhang, Zhihuai Zhang, Weiqian xiong, Shisheng Zhao, Yudi Fudan Univ Sch Informat Sci & Technol Micronano Syst Ctr Shanghai Peoples R China Beijing Informat Sci & Technol Univ Minist Informat Ind Key Lab Informat & Commun Syst Beijing Peoples R China

ISBN: (纸本)9781665450737

Different from binary computation, stochastic computation (SC), as a new paradigm, uses stochastic bit stream (SBS) to encode data. By simplifying computing elements, the circuit area can be greatly reduced. SBS can be generated by a stochastic number generator (SNG) with a variety of formats. In this work, we use unipolar (UP) and bipolar (BP) formats to optimize the traditional SC subtractor, which is named the UP-to-BP Subtractor (UBS). A new cross format coding (CFC) method is proposed for stochastic computing, which combines the UP and BP format, and is applied to Sobel edge detection in image processing algorithms. The fault tolerance and detection efficacy of the proposed CFC method and conventional binary computing are compared in this paper. By using the CFC method, the detected F-Score is improved by 0.15(23%). If the F-score remains unchanged, the processing speed can be about 10 times faster.

关键词： stochastic computing stochastic subtractor image processing edge detection

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FlowValve: Packet Scheduling Offloaded on NP-based SmartNICs 42

FlowValve: Packet Scheduling Offloaded on NP-based SmartNICs

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42nd IEEE International conference on Distributed Computing systems (ICDCS)

作者： xi, Shaoke Li, Fuliang Wang, xingwei Northeastern Univ Shenyang Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Enforcing scheduling policies at end-hosts with software schedulers suffers from high CPU consumption, low throughput, and inaccuracy. Offloading scheduling functions to the network interface card (NIC) provides a promising direction to address these problems. However, existing efforts in scheduling offloading suffer from inflexible on-NIC packet schedulers, which cannot execute complex hierarchies of network policies. In this paper, we present FlowValve, the first parallel packet scheduler for Network Processor (NP)-based SmartNlCs that offloads critical functions of Linux traffic control, including packet classifying and scheduling. The key insight behind FlowValve is to abstract inherent queues attached to the NIC interface (wire side) as a single FIFO queue and perform specialized tail drop to mix the FIFO queue with expected flow proportions. FlowValve takes advantage of on-chip multi-core parallelism and hardware accelerations to produce high throughput. Meanwhile, it substantially reduces CPU and memory burdens on endhosts. We prototype FlowValve on a Netronome Agilio SmartNIC and demonstrate its effectiveness against non-offloaded kernel schedulers and DPDK QoS Scheduler. We find that FlowValve outperforms both in accurately enforcing network policies while driving line rate performance (i.e., 40Gbps), which contributes to saving at least two CPU cores.

关键词： Scheduling algorithms Multicore processing Linux Wires Tail Traffic control Parallel processing

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Efficiency of deep neural networks for reinforced concrete shear walls failure mode detection 27

Efficiency of deep neural networks for reinforced concrete s...

引用

27th International Computer conference, Computer Society of Iran, CSICC 2022

作者： Barkhordari, Mohammad Sadegh Massone, Leonardo M. Department Of Civil And Environmental Engineering Iran University Of Chile Department Of Civil Engineering Santiago Chile

ISBN: (纸本)9781665480277

After a severe earthquake, it is critical to check the health of hospitals, buildings, fire stations, to name but a few. New technologies such as deep learning algorithms, image processing, and signal processing methods have become more appealing in such quick applications because of their higher reliabilities and efficiency than traditional visual methods. On the other hand, reinforced concrete shear walls are the key structural elements of the most buildings as lateral force resisting systems in seismic areas. Determining the failure mode of reinforced concrete shear walls is a crucial task that can help engineers and designers choose the best retrofitting options. The goal of this research is to assess the ability of deep neural networks for predicting the failure mode of the reinforced concrete shear walls. Furthermore, the SHapley Additive exPlanation approach is used to demonstrate the relevance and contribution of the factors that influence the reinforced concrete shear walls failure mode determination. © 2022 IEEE.

关键词： Deep learning Visualization Buildings Neurons Signal processing algorithms Signal processing Concrete

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An Efficient image-Based Virtual Scene Generation and Stitching Algorithm

An Efficient Image-Based Virtual Scene Generation and Stitch...

引用

Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Luo Wei Chongqing Three Gorges Vocational College Chongqing Wanzhou China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the development of computer graphics and image processing, the virtual scene generation and splicing technology has been widely used in various fields of computer-aided image analysis. In this context, this study proposes a new and efficient image stitching algorithm to solve the image stitching artifacts caused by perspective changes and exposure differences in multi-camera systems, and expands it to the field of virtual images. First, as a pre-processing part, the model analyzes the image through nonlinear filtering and mesh deformation optimization to ensure image quality. Subsequently, the model presents a new image stitching algorithm based on the image matching method of minimizing projection deviation and thin plate spline theory. This algorithm further proposes an improved EIEOWF algorithm based on circular weighted fusion to achieve the task of smooth transition stitching of images. Finally, the stitching technology was extended to virtual scene generation, and OpenGL technology was used to achieve real-time 3D texture mapping. The test part is verified through experiments on the UDIS-D data set. The results show that the average splicing accuracy of the proposed algorithm in 100 sets of tests is 95%, with a standard deviation of 2%, which is significantly better than the existing comparison algorithm.

关键词： Three-dimensional displays Accuracy Splicing Heuristic algorithms Reliability theory Real-time systems Splines (mathematics) image stitching Standards Optimization

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Application of Deep Learning Based on Edge Computing of Wireless Sensor Networks

Application of Deep Learning Based on Edge Computing of Wire...

引用

Advanced algorithms and Control Engineering (ICAACE), International conference on

作者： Huaqiao Duan Qijing Wang Nanjing University Suzhou China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

With the rapid advancement of the Internet of Things (IoT) technology, Wireless Sensor Networks (WSN) have become a fundamental component of contemporary society. Nevertheless, the surge in complex application scenarios coupled with an exponential increase in data volumes has rendered the traditional cloud computing processing model insufficient. The edge computing-based deep learning framework can effectively handle these large-scale image datasets without compromising privacy and security, facilitating intelligent analysis and decision-making. This study,through experimental design, we developed a deep learning model grounded in edge computing, tailored for addressing specific challenges in image data processing within wireless sensor networks, such as image recognition, classification, and analysis. For concrete metrics, consider a scenario where edge computing-based deep learning models were deployed for real-time traffic flow analysis. These models, by processing data locally, can reduce data transmission times by up to 70%, enhancing response times from seconds to milliseconds. Furthermore, in privacy-sensitive applications such as wildlife monitoring, edge computing ensures data is processed on-site, reducing the risk of sensitive data exposure by over 80%. Lastly, in terms of energy consumption, edge devices operating on optimized deep learning algorithms can achieve a reduction of up to 40% compared to traditional cloud-based processing methods, underlining the significant efficiency and performance gains achievable with this technology integration.

关键词： Deep learning Wireless sensor networks Cloud computing Computational modeling image edge detection Data processing Data models Real-time systems Time factors Edge computing

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Overview of image Detection algorithms: Performance Analysis and Technology Trends

Overview of Image Detection Algorithms: Performance Analysis...

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International conference on Informatics and Computing (ICIC)

作者： Nurul Hidayat R. Rizal Isnanto Department of Informatics Jenderal Soedirman University Purwokerto Indonesia Department of Computer Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

image detection plays a vital role in digital image processing and artificial intelligence, with applications ranging from security surveillance to autonomous vehicles and medical image analysis. This study employs a qualitative approach to review the performance of image detection algorithms, focusing on deep learning methods such as YOLO, SSD, and CNN. Through computational experiments using quantitative data, this research provides performance comparisons based on accuracy, precision, recall, and computational efficiency. The results highlight YOLO’s superior performance in terms of both accuracy ($\mathbf{9 2} .5 \%$) and inference speed (75.2 fps), making it suitable for real-time applications. This study contributes by addressing the gap in balancing computational efficiency and adaptability for real-world applications.

关键词： YOLO Deep learning Machine learning algorithms Accuracy Computational modeling Real-time systems Inference algorithms Computational efficiency Detection algorithms Edge computing

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