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检索条件"主题词=detection algorithms"

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Drone detection and Classification Based on Radar Cross Section Signatures

Drone Detection and Classification Based on Radar Cross Sect...

引用

International Symposium on Antennas and Propagation (ISAP)

作者： Vasilii Semkin Mingsheng Yin Yaqi Hu Marco Mezzavilla Sundeep Rangan VTT Technical Research Centre of Finland Ltd Finland NYU Tandon School of Engineering Brooklyn NY USA

ISBN: (数字)9784885523267

ISBN: (纸本)9781728159096

In this work, we show how drone detection and classification can be enabled by leveraging a database of radar cross section (RCS) signatures. First, we present a set of measurement results of the RCS of a carbon fiber drone model at 28 GHz. The measurements were performed in an anechoic chamber and provide essential information about the RCS signature of the specific drone. Then, we assess the RCS-based detection probability and the range error by running simulations in urban environments. The drones were positioned at different distances, from 30m to 90m, and the RCS signatures used for the detection and classification were obtained experimentally.

关键词： Antenna measurements Radar cross-sections Databases Urban areas Machine learning detection algorithms Drones

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detection of Residual Yarn in Bobbin Based on Hough Transform and BP Neural Network

Detection of Residual Yarn in Bobbin Based on Hough Transfor...

引用

IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Ran Hu Zehuai Fu Shuwei Zhu Xiaolong Liu Fang Jia School of Mechanical Engineering Southeast University Nanjing China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

In order to promote the intellectualization of textile industry, in this paper, a step-by-step residual yarn detection algorithm based on Hough transform and BP neural network is proposed. According to the geometric characteristic's transformation of bobbins with a large amount of residual yarn, the images are converted to the grayscale, then the edges of image are detected by Canny-Otsu algorithm and the outlines of bobbin are detected by Hough transform, the bobbins with a large amount of residual yarn are eliminated by outline detection results. The images which cannot eliminated by outline detection are converted to HSV color space and divide to several sub regions, the HSV values are extracted as the input layer of BP neural network, the residual yarn condition of bobbins as the output layer. The experimental results show that the detection accuracy of algorithm is up to 96%, which meets the requirements of practical application.

关键词： Feature detection Neural networks Transforms Classification algorithms Yarn detection algorithms Textile industry

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Leader and breakaway detection in racing sports videos

Leader and breakaway detection in racing sports videos

引用

IEEE Workshop on Multimedia Signal Processing

作者： Sotirios Papadopoulos Charalampos Symeonidis Ioannis Pitas Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9781665432894

This paper addresses the important problem of leader detection in racing sports videos (e.g., cycling, boating and car racing events), as his/her proper framing is a pivotal issue in racing sports cinematography, where the events have a linear spatial deployment. Over the last few years, as autonomous drone vision and cinematography emerged, new challenges appeared in drone vision. While, until recently, most computer vision methods typically addressed still camera AV footage, drone sports cinematography typically employs moving cameras. In this paper, we solve the problem of leader detection in a group of similarly moving targets in sports videos, e.g. the leader of a sports cyclist group and his/her breakaway during a cycling event. This is very useful in drone sports cinematography, as it is important that the drone camera automatically centers on such a leader. We demonstrate that the novel method described in this paper can effectively solve the problem of leader detection in sports videos.

关键词： Target tracking Signal processing algorithms Cinematography Signal processing Cameras Trajectory detection algorithms

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Label propagation community detection algorithm based on density peak optimization

Label propagation community detection algorithm based on den...

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International Conference on Computational Intelligence and Security

作者： Ma Yan Chen Guoqiang School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781665494908

Label propagation algorithm is one of the popular community detection algorithms in recent years. The advantages of the community detection algorithm based on label propagation are that the algorithm logic is simple, compared with the modularity optimization algorithm, the convergence speed is very fast, the whole clustering process does not need any optimization function, and there is no need to specify the number of communities in the complex network before initialization. However, this algorithm has problems such as unstable partitioning results and strong randomness. In order to solve these problems, this paper proposes a semi-supervised label propagation community detection algorithm based on density peak. The proposed algorithm first introduces the density peak to discover the cluster centers, determines the prototype of community, fixes the number of communities and cluster centers in complex network, and then uses label propagation algorithm to detect communities, which improves the accuracy and robustness of community discovery, reduces the number of iterations, and accelerates the formation of the communities.

关键词： Clustering algorithms Prototypes Complex networks Robustness Partitioning algorithms Security detection algorithms

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Real-time Amplitude and Phase detection of Grid Fundamental Voltage Considering Decaying DC Component

Real-time Amplitude and Phase Detection of Grid Fundamental ...

引用

IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Hongqing Liu Jianxin Zhang Yang Qian School of Electrical and Power Engineering China University of Mining and Technology Xuzhou China School of Electrical Engineering Xinjiang University Urumqi China School of Eenergy And Power Engineering Nanjing Institute of Technology Nanjing China

The fast and accurate fundamental-wave amplitude and phase detection of the grid voltage can be realized by filtering the decayed DC (DDC) component signal effectively. To this end, firstly, based on the periodicity and half-wave symmetry of the three-phase grid voltage signals, the grid voltage is sampled at a half-period and full-period delay under the abc frame system to achieve the DDC component detection in one grid-period. Secondly, aiming at the distorted signals containing both DDC and harmonic components, the DDC detection algorithm and the moving average filtering algorithm are combined in the dq frame system to achieve the simultaneous filtering of DDC and harmonic components in one grid cycle. Finally, the proposed detection algorithm is combined with the grid voltage amplitude/phase detection algorithm to obtain a complete amplitude phase detection algorithm for the fundamental grid voltage. Experiment results verified the effectiveness and advancement of the proposed detection algorithm.

关键词： Power quality Protective relaying Filtering algorithms Harmonic analysis Real-time systems Time factors detection algorithms

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Expression Tree-based Policy Conflict detection Algorithm

Expression Tree-based Policy Conflict Detection Algorithm

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International Conference on Networking and Network Applications (NaNA)

作者： Xue Wang Hao Zhang Kaijun Wu Science and Technology on Communication Security Laboratory Chengdu China No.30 Research Institute of China Electronics Technology Group Corporation Chengdu China China Electronics Technology Cyber Security Co. Ltd Chengdu China

In attribute-based access control services, there are problems such as cumbersome policy control, prone to authorization conflicts, and conflicts caused by complex attribute structures are not easy to identify. Due to the difficulty of the conflict detection problem, most of the detection methods have strict requirements for policy structure. Normally, the priority strategy are chosen uniformly when using the directed acyclic graph approach to disambiguate conflict rule pairs. The present methods are not thorough, flexible and user-friendly for the policy design in practical applications. To address these problems, an access control policy conflict detection algorithm based on intersection of target expression trees under the XACML (eXtensible Access Control Markup Language) specification is proposed. The method efficiently locates the conflict rule pairs based on the index structure through policy tree and rule effects, determines the conflict by expression comparison n, and marks the possible causes of the conflict, provides analysis of the disambiguation scheme, and achieves access control with fine granularity.

关键词： Authorization Directed acyclic graph Markup languages Redundancy Indexes detection algorithms

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Online Changepoint detection on a Budget

Online Changepoint Detection on a Budget

引用

IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Zhaohui Wang Xiao Lin Abhinav Mishra Ram Sriharsha Applied Research Splunk San Francisco US

ISBN: (纸本)9781665424288

Changepoints are abrupt variations in the underlying distribution of data. Detecting changes in a data stream is an important problem with many applications. In this paper, we are interested in changepoint detection algorithm which operates in an online setting in the sense that both its storage requirements and worst-case computational complexity per observation are independent of the number of previous observations. We propose an online changepoint detection algorithm for both univariate and multivariate data which compares favorably with offline changepoint detection algorithms while also operating in a strictly more constrained computational model. In addition, we present a simple online hyperparameter auto tuning technique for these algorithms.

关键词： Conferences Computational modeling Throughput Data models Bayes methods Data mining detection algorithms

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Intelligent Crater detection on Planetary Surface Using Convolutional Neural Network

Intelligent Crater Detection on Planetary Surface Using Conv...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Yitian Wu Gang Wan Lei Liu Zhanji Wei Shuai Wang Space Engineering University Beijing China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

Crater extraction and recognition is an important research content of deep space planetary science. Traditional crater detection algorithms (CDAs) are mainly based on crater feature construction which relies on high-quality data. With the application of deep learning in image recognition and semantic segmentation, new ideas have been brought to meteorite crater extraction. Many crater extraction algorithms based on artificial intelligence have been proposed which greatly simplifies the crater extraction process and improves detection accuracy. However, with the improvement of the accuracy, the convolution kernels become more and more, and the huge parameters and the consumption of storage and computing resources limit the application of these algorithms in mobile device. In order to solve this problem, we propose a compact crater extraction network based on model pruning. In the combination of U-Net and residual block, the network structure is optimized under the premise of retaining the longitude of large model extraction, and the balance of hardware resources and algorithm accuracy is achieved. The experimental results show that we compress the model to 4.9% of raw size and only lose almost 0.5% accuracy. It provides a reference for the application of CDAs on resource constrained platforms.

关键词： Computational modeling Semantics Neural networks Feature extraction Mobile handsets detection algorithms Optimization

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Generalized Iris Presentation Attack detection Algorithm under Cross-Database Settings

Generalized Iris Presentation Attack Detection Algorithm und...

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International Conference on Pattern Recognition

作者： Mehak Gupta Vishal Singh Akshay Agarwal Mayank Vatsa Richa Singh IIIT-Delhi India Texas A&M University Kingsville USA IIT Jodhpur India

Presentation attacks are posing major challenges to most of the biometric modalities. Iris recognition, which is considered as one of the most accurate biometric modality for person identification, has also been shown to be vulnerable to advanced presentation attacks such as 3D contact lenses and textured lens. While in the literature, several presentation attack detection (PAD) algorithms are presented; a significant limitation is the generalizability against an unseen database, unseen sensor, and different imaging environment. To address this challenge, we propose a generalized deep learning-based PAD network, MVANet, which utilizes multiple representation layers. It is inspired by the simplicity and success of hybrid algorithm or fusion of multiple detection networks. The computational complexity is an essential factor in training deep neural networks; therefore, to reduce the computational complexity while learning multiple feature representation layers, a fixed base model has been used. The performance of the proposed network is demonstrated on multiple databases such as IIITD-WVU MUIPA and IIITD-CLI databases under cross-database training-testing settings, to assess the generalizability of the proposed algorithm.

关键词： Training Three-dimensional displays Databases Neural networks Imaging Pattern recognition detection algorithms

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Mobilenetv3-YOLOv4-Sonar: Object detection Model Based on Lightweight Network for Forward-Looking Sonar Image

Mobilenetv3-YOLOv4-Sonar: Object Detection Model Based on Li...

引用

OCEANS

作者： Xiufen Ye Weizheng Zhang Yuanzi Li Wenyang Luo College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9781665427883

Real-time performance and accuracy are the standards to measure the detection algorithm performance. The existing detection methods for forward-looking sonar target image are difficult to achieve ideal balance between the two standards. Due to the influence of background interference and non-rigid deformation, it hardly obtains the high detection accuracy in fast detection. In this paper, we propose a Mobilenetv3-YOLOv4-Sonar algorithm, which is a more accurate and lightweight target detection model. The backbone of YOLOv4 adopts Mobilenetv3, which is improved by Convolutional Block Attention Module (CBMA) and modified Squeeze-and-Excitation Network (SENet). Consequently, we can eliminate the influence of high-light background interference and reduce the complexity of the model. Meanwhile, the improved SPPnet is used as the neck of YOLOv4 to reduce the failure of target detection caused by non-rigid deformation. Extensive experiments and comparisons with state-of-arts detectors illustrate that the accuracy of our model has increased by 4.6% compared with YOLOv4 on the dataset, and the model parameters are only one tenth of the traditional YOLOv4, which has practical significance for target detection in forward-looking sonar images.

关键词： Oceans Sonar measurements Sonar equipment Object detection Interference Real-time systems detection algorithms

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