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检索条件"主题词=detection algorithms"

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Infrared Target detection Based on the Fusion of Mask R-CNN and Image Enhancement Network

Infrared Target Detection Based on the Fusion of Mask R-CNN ...

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Chinese Automation Congress (CAC)

作者： Fang Liu Song Guan Kuaikuai Yu Hua Gong College of Science Shengyang Ligong University Shenyang China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjing China

In this paper, we focus on the problem of the infrared image detection. Aiming at the problem of the low contrast of the infrared images, an adaptive gamma transformation algorithm (AGT) is established to intelligently improve the contrast of the infrared images. Aiming at the problem of the weak edge information of the infrared images, an improved Sobel operator edge enhancement algorithm (ISOEE) is designed to enrich the edge features of the infrared images. In order to solve the problem that the traditional infrared image detection algorithm relies on expert experience, the fusion infrared images detection model AIE-Mask R-CNN based on Mask R-CNN, AGT algorithm and ISOEE algorithm is proposed. This fusion model can realize accurate and intelligent detection for the infrared images. The experimental results show that the fusion model AIE-Mask R-CNN improves the detection accuracy of the infrared images.

关键词： Adaptation models Automation Image edge detection Object detection detection algorithms Image enhancement

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Sperm Manipulation and Selection Platform Using Optoelectronic Tweezers*

Sperm Manipulation and Selection Platform Using Optoelectron...

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IEEE International Conference on Robotics and Biomimetics

作者： Shunxiao Huang Jiawei Zhac Chunyuan Gan Hongyi Xiong Jingwen Ye Hongyan Sun Wenyan Niu Ao Wang Bo Chen Zijin Zeng Chan Li Lin Feng Beihang University School of Mechanical Engineering Automation Beijing China Beijing Advanced Innovation Center for Biomedical Engineering and School of Mechanical Engineering Automation Beijing China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

A sperm manipulation and selection platform using optoelectronic tweezers is proposed in this paper, fully leveraging the high throughput, damage-free, and contactless advantages of optoelectronic tweezers, combined with advanced computer vision techniques, to achieve precise manipulation and selection of sperm. Initially, through simulation, the force conditions of sperm under the optoelectronic tweezers are studied, followed by the development of a sperm detection algorithm based on YOLOv8, ByteTrack, and average speed strategies. This leads to the successful construction of a sperm manipulation and selection system encompassing detection, tracking, speed measurement, and manipulation. Experimental results demonstrate that the system can achieve high-precision manipulation of individual sperm, rapidly and orderly ar-range multiple sperms, and effectively select sperm based on morphology, speed, and vitality. Additionally, the system can significantly increase sperm concentration in a short period and effectively transport groups of sperm. With the increasing problem of infertility, particularly the decline in male sperm quality, this research provides an effective technical means for the development of efficient sperm manipulation and selection technology.

关键词： Tracking Integrated optoelectronics Force Morphology Switches Fluorescence Throughput Control systems Velocity measurement detection algorithms

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Detecting Wildfires Using Satellite Imagery

Detecting Wildfires Using Satellite Imagery

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Animesh Singh Parul Madan Pradeep Kumar Arya Vasim Sheikh Mukul Gupta Rupansh Kumar Graphic Era Deemed to be University Dehradun India Bennett University Graeter Noida India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

The research “Wildfire detection from Satellite Images” addresses the formidable challenge of automating the classification of diverse visible range satellite images through an innovative integration of machine learning and computer vision using custom dataset. In contrast to conventional methods relying on manual observations, this pioneering venture leverages a meticulously curated dataset representing a broad spectrum of satellite images in various environmental contexts. The binary classification model, at the research's core, combines the strengths of diverse machine learning algorithms, employing ensemble learning techniques to enhance accuracy and versatility across different ecological conditions. Climate action is a key Sustainable Development Goal (SDG-13) of the United Nations (UN), and wildfires are one of the calamitous events that is both caused by and contribute to climate change. Large scale wildfires have increased significantly in frequency in Australia and other wildfire prone nations in recent years. These fires pose a threat to urban and global forests, inflict a significant amount of harm to property and the environment, and frequently result in fatalities. There is a continuous discussion about how to manage wildfires and lessen their social, economic, and environmental effects. With different rising monitoring systems and various applications of those system emerge to observe the ecology and environment of the globe. This research showcases the intricate relation of technology with nature. By deploying machine learning/ deep learning model to observe different events of the globe, we can easily develop system to detect any drastic climate event such as Wildfires and take preventive measures at early stages reducing causality and losses.

关键词： Climate change Data models Computer vision Image recognition Wildfires Satellite images detection algorithms

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LSTD: Stable Triangle Descriptor of Line Keypoints for Loop Closing in 3D Lidar SLAM

LSTD: Stable Triangle Descriptor of Line Keypoints for Loop ...

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Chinese Automation Congress (CAC)

作者： Zheng Tan Lingli Yu Jianzhi Zhou Weiwei Zhu School of Automation Central South University Chang Sha China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Loop closing is a crucial module in simultaneous localization and mapping(SLAM), enabling the identification of revisited locations and facilitating trajectory correction. However, the high dimensionality, sparsity, and susceptibility to occlusions of LiDAR-generated point clouds pose significant challenges for this task. We propose Stable Triangle Descriptor of Line Keypoints(LSTD), which extracts texture-rich line features as keypoints and constructs stable triangular features. This descriptor encapsulates both geometric and spatial characteristics, and remains resilient to changes in LiDAR viewing angles, thereby enhancing loop closing capabilities. We devise a voting-based scheme for descriptor matching and leverage the circumscribed circle feature of triangles to enable efficient loop closure detection. Comparative evaluations on the KITTI dataset demonstrate the improvements offered by our approach. Furthermore, we integrate the algorithm into LiDAR SLAM and validate its efficacy on the KITTI dataset as well as our own dataset, showcasing its ability to accurately identify loops and refine the system's trajectory and point cloud map.

关键词： Point cloud compression Location awareness Visualization Laser radar Simultaneous localization and mapping Three-dimensional displays Feature extraction Prediction algorithms Trajectory detection algorithms

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Research on Grayscale Images Flame detections Based on Colorazion Algorithm and YOLOv8

Research on Grayscale Images Flame Detections Based on Color...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Peng Liu Zhenfu Sun Biao Zhou Hongyu Wang Haozhen Qu Institute of Energy and Electrical Engineering Changchun University of Science and Technology Changchun China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

Flame detection algorithms are crucial for real-time fire monitoring using surveillance cameras. Current flame detection algorithms perform excellently on color cameras; however, many night vision cameras can only capture grayscale images, making flame detection less effective in such scenarios. Traditional flame detection algorithms rely heavily on color features, especially the red and yellow hues of flames. Conversely, grayscale images lack color information, making these algorithms ineffective. To address this limitation, we propose a novel flame detection algorithm that integrates the DeOldify model with the you only look once(YOLOv8) model. DeOldify can effectively add color to grayscale images. YOLOv8 is a powerful object detection model that excels in identifying various objects within an image. This algorithm leverages DeOldify's colorization capabilities and YOLOv8's powerful object detection abilities to enhance flame detection accuracy in grayscale images. This algorithm has been tested multiple times on public datasets, converting original images to grayscale and then colorizing them to construct the dataset. The results show that, compared to grayscale images, this algorithm improves the mean average precision (mAP@0.5) by 19.7% on different validation sets, by 4% on different training sets, and by 12.9% on grayscale images captured by night vision cameras. By comparing the training results of models such as YOLOv8n and YOLOv8s, the colorized images improve the model's performance. This demonstrates the high generalizability of this algorithm.

关键词： Night vision YOLO Training Image color analysis Surveillance Fires Gray-scale Cameras Real-time systems detection algorithms

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A GPGPU-Based Algorithm Acceleration System for ECG Signal Processing

A GPGPU-Based Algorithm Acceleration System for ECG Signal P...

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algorithms, Computing and Artificial Intelligence (ACAI), International Conference on

作者： Tianyu Huang Zhijiong Wang Guannan Hu Sio Hang Pun Hung Chun Li Mang I Vai Department of Electrical and Computer Engineering Joint Laboratory of Zhuhai UM Science and Technology Research Institute - Lingyange Semiconductor Incorporated University of Macau Macao China Department of Electrical and Computer Engineering Joint Laboratory of Zhuhai UM Lingyange Semiconductor Incorporated Science and Technology Research Institute - Lingyange Semiconductor Incorporated University of Macau Macao China Joint Laboratory of Zhuhai UM Lingyange Semiconductor Incorporated Science and Technology Research Institute - Lingyange Semiconductor Incorporated Zhuhai China Department of Electrical and Computer Engineering Joint Laboratory of Zhuhai UM Institute of Microelectronics Science and Technology Research Institute - Lingyange Semiconductor Incorporated University of Macau Macao China

ISBN: (数字)9798331529314

ISBN: (纸本)9798331529321

Electrocardiogram (ECG) is an important non-invasive technique for diagnosing cardiovascular diseases (CVD). After acquiring the patients' raw ECG signal data, signal processing is essential for the diagnosis. Conventional ECG signal processing is usually performed serially, which takes a long time to process signals with large amounts of data. In this study, we have proposed a GPGPU-based algorithm acceleration system to improve the efficiency of ECG signal processing. This system utilizes OpenCL for parallel algorithm development on GPGPU. GPGPU executes certain steps in the QRS complex detection algorithm via parallel computing, thereby accelerating the algorithmic process. Experimental results showed that the system achieved a speedup of 1.279 compared to serial computing when processing 39 ECG signal sequences in parallel. In addition, our system has been equipped with a progressive transmission mechanism and a function that intelligently determines whether an algorithm can be accelerated. These features make this system practical not only in biomedical signal processing but also in engineering and research fields with large amounts of data.

关键词： Signal processing algorithms Signal processing Electrocardiography Cardiovascular diseases Biomedical communication Parallel algorithms detection algorithms Artificial intelligence

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Identifying Hierarchical Community Structures in Content-Based Scholarly Social Networks

Identifying Hierarchical Community Structures in Content-Bas...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Md Asaduzzaman Noor John W. Sheppard Jason A. Clark Gianforte School of Computing Montana State University Bozeman Montana USA Library Montana State University Bozeman Montana USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Community detection plays a pivotal role in social network analysis by partitioning networks into cohesive groups of vertices with dense intra-group connections and sparse inter-group connections. In this paper, we utilized a scholarly social network based on researchers' topic similarity derived from their publication metadata to identify interdisciplinary research communities. As topics often form a hierarchy, we hypothesize that the constructed scholarly network will exhibit hierarchical community structures. Therefore, we explore the efficacy of two prominent community detection algorithms, Louvain and Spectral clustering, known for their capacity to detect hierarchical community structures within networks. While both algorithms demonstrate this capability, the original Louvain algorithm is susceptible to the resolution limit problem due to its reliance on the modularity measure. To address this limitation, we propose the nested hierarchical Louvain algorithm, which iteratively partitions the network based on previously identified subgraphs, and we find that the bias towards large communities is mitigated. To evaluate the hierarchy produced by each of the algorithms, we employ the Cophenetic Correlation Coefficient (CPCC), a metric commonly used in hierarchical clustering evaluations but less frequently utilized in hierarchical community analysis. We argue that CPCC can be a useful measure to identify the presence of implicit hierarchical community structure in social networks when it is not explicitly available from domain knowledge while also further mitigating the inherent bias present in using modularity as a metric. Experimental results, conducted on both synthetic networks and the scholarly social network, demonstrate that the nested hierarchical Louvain algorithm, as well as Spectral Clustering, successfully identifies more finely structured hierarchical communities, offering greater depth in the dendrogram compared to the basic Louvain algorithm.

关键词： Measurement Knowledge engineering Correlation coefficient Machine learning algorithms Social networking (online) Clustering algorithms Machine learning Metadata Partitioning algorithms detection algorithms

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Image Recognition of Rice Growth Period Based on Convolutional Neural Network

Image Recognition of Rice Growth Period Based on Convolution...

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Computer Engineering and Intelligent Control (ICCEIC) International Conference on

作者： Tianci Liu College of Mechanical and Electrical Engineering Wuhan Donghu University Wuhan Hubei Province China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

In the field of crop image detection, traditional detection methods for the growth period of related crops rely heavily on humans, resulting in relatively low accuracy. At present, the mainstream spectral detection technology based on remote sensing images of crops has high hardware requirements for image acquisition and a complex system. In order to realize a crop growth period image detection method with high accuracy, convenient operation, and fast speed. A crop convolutional neural network (Crop-Net) is proposed to train and test rice images at different growth stages. Experimental results show that the proposed detection algorithm has higher detection accuracy in the detection task of ten growth periods of rice.

关键词： Measurement Accuracy Image recognition Crops Hardware Convolutional neural networks detection algorithms Complex systems Remote sensing Intelligent control

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Introducing the Modularity Graph: An Application to Brain Functional Networks

Introducing the Modularity Graph: An Application to Brain Fu...

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European Signal Processing Conference (EUSIPCO)

作者： Tiziana Cattai Camilla Caporali Marie-Constance Corsi Stefania Colonnese Dept. of Information Engineering Electronics and Telecommunication Sapienza University of Rome Italy Sorbonne Universite Institut du Cerveau - Paris Brain Institute -ICM CNRS Inria Inserm AP-HP Hopital de la Pitié Salpêtriére Paris France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In signal processing, exploring complex systems through network representations has become an area of growing interest. This study introduces the modularity graph, a new graph-based feature, to highlight the relationship across the graph communities. After showing an application to the random graph class known as Stochastic Block Model, we consider the brain functional connectivity network estimated from real EEG data. The modularity graph provides a quantitative framework for examining the interactions between neuron clusters within the brain's network. The modularity graph works alongside multiscale community detection algorithms, thereby enabling the identification of community structures at various scales. After introducing the modularity graph, we apply it to the brain functional connectivity network, estimated from publicly available EEG recordings of motor imagery experiments. Statis-tical analysis across multiple scales shows that the modularity graph differs for the distinct brain connectivity states associated with various motor imagery tasks. This work emphasizes the application of signal on graph processing techniques to understand brain behavior during specific cognitive tasks, leveraging the novel modularity graph to identify patterns of brain connectivity in different cognitive conditions. This approach sets the stage for further signal on graph analysis to devise brain network modularity, and to gain insights into the motor imagery mechanisms.

关键词： Neurons Stochastic processes Europe Motors Brain modeling Electroencephalography Data models Recording detection algorithms Complex systems

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Comprehensive Influence-Based Social User Hiding Scheme in Overlapping Community Networks

Comprehensive Influence-Based Social User Hiding Scheme in O...

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Next Generation Data-driven Networks (NGDN), International Conference on

作者： Jian Liu Ke Gu Yang Deng Jiaqi Lei School of Computer and Communication Engineering Changsha University of Science & Technology ChangSha China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

As overlapping community detection algorithms continue to mature, the issue of information being over-mined has also become apparent. Existing overlapping community hiding algorithms are inadequate. Therefore, in this paper, we propose a user hiding and community hiding scheme based on node comprehensive influence, which achieves user hiding and community hiding by adjusting user link relations. First, we propose a node comprehensive influence calculation method based on K-Shell decomposition and improved particle competition mechanism. On the basis of comprehensive influence of node, we construct a scheme to adjust link relations. Finally, relevant experiments prove that our scheme can effectively achieve user hiding and user group hiding.

关键词： detection algorithms Next generation networking Community networks

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