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检索条件"主题词=detection algorithms"

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Improved Wheat Spike Recognition Method Based on YOLOv5 with Added Channel Attention Mechanism

Improved Wheat Spike Recognition Method Based on YOLOv5 with...

引用

Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Li Zhao Zhihong Wang Fang Jia Runfeng Li Lixin Liu Xinyue Zhang Shandong Agriculture and Engineering University Jinan China Shandong Institute of Information Technology Industry Development Jinan China

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

Rapid identification and detecting wheat spikes is of great significance for understanding the growth status of wheat, evaluating its growth potential, and estimating future wheat yields. Traditional object detection algorithms often face a trade-off between accuracy and speed. In order to improve the accuracy of wheat spike recognition, this study used an improved one-stage object detection algorithm Yolov5_SE algorithm, which introduced the channel attention mechanism module SE_Block to enhance the focus on local detailed features of wheat spikes and solve the problems of dense and overlapping wheat spikes. This paper continuously changed the position and number of SE_Block in the original network structure when adding the channel attention mechanism to the improved Yolov5 algorithm. The experimental results show that the proposed algorithm in wheat spike recognition tasks achieved better performance, with an average precision (mAP) improvement of 0.8% compared to the original Yolov5 algorithm, while speed was also improved. The results of this study can provide reference and inspiration for the application of machine vision technology in the field of agriculture.

关键词： YOLO Attention mechanisms Accuracy Target recognition Machine vision Face recognition Feature extraction Agriculture Intelligent systems detection algorithms

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Automatic Analysis of Thermal Images to Detect Hot Spot Regions

Automatic Analysis of Thermal Images to Detect Hot Spot Regi...

引用

Information Technology and Data Science (CITDS), IEEE Conference on

作者： Norbert Serban Balazs Harangi Faculty of Informatics University of Debrecen Debrecen Hungary

ISBN: (数字)9798350387889

ISBN: (纸本)9798350387896

This paper presents an automated system that uses an RGB image and thermal data from a thermal camera. We aim to detect hot spot areas and fuse the two images into one to highlight the hot spot regions on the RGB image. With this solution, it is possible to identify any inflamed areas and examine the healing process after surgery. The paper discusses an image registration method, a hot spot detection algorithm that can detect abnormal hot spot regions, and a fusion algorithm that can highlight the interesting areas on the fused image.

关键词： Measurement Image registration Machine learning algorithms Clustering algorithms Surgery Machine learning Thermal analysis detection algorithms Information technology Image fusion

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An improved algorithm for regional cattle population counting under local optimal methods

An improved algorithm for regional cattle population countin...

引用

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

作者： Bingxuan Li Jiandong Fang Yudong Zhao College of Information Engineering Inner Mongolia University of Technology Hohhot China Inner Mongolia Key Laboratory of Perceptive Technology and Intelligent Systems Hohhot China Inner Mongolia Autonomous Region Intelligent Agriculture and Animal Husbandry Perceptual Technology Collaborative Innovation Center Hohhot China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

As a movable collateral, live cattle need to be accurately counted and recorded on a regular basis, such as their breeds, numbers and other important information. Aiming at the problems that the existing cattle number detection algorithms have leakage detection phenomenon for dense and small targets and have many parameters and large computation volume, which are not suitable to be deployed at the end of embedded devices, a CBG-YOLOv5s cattle number detection algorithm is proposed. The experimental results show that the improved model reduces the number of parameters and computation by 37.32% and 43.04%, respectively, compared with the original model YOLOv5s, and still maintains good detection accuracy. Finally, the lightened model was successfully deployed to Nvidia Jetson Nano mobile and verified by actually running on the development board. The model in this paper is able to ensure the accuracy of cattle identification, which can provide a reference for the accuracy of detecting the number of cattle in complex farming environments and under severe occlusion conditions.

关键词： YOLO Accuracy Target tracking Computational modeling Signal processing algorithms Cows Signal processing Real-time systems Object recognition detection algorithms

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ICGNet: A Unified Approach for Instance-Centric Grasping

ICGNet: A Unified Approach for Instance-Centric Grasping

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IEEE International Conference on Robotics and Automation (ICRA)

作者： René Zurbrügg Yifan Liu Francis Engelmann Suryansh Kumar Marco Hutter Vaishakh Patil Fisher Yu ETH Zürich Visual Computing School of PVFA Texas A&M University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Accurate grasping is the key to several robotic tasks including assembly and household robotics. Executing a successful grasp in a cluttered environment requires multiple levels of scene understanding: First, the robot needs to analyze the geometric properties of individual objects to find feasible grasps. These grasps need to be compliant with the local object geometry. Second, for each proposed grasp, the robot needs to reason about the interactions with other objects in the scene. Finally, the robot must compute a collision-free grasp trajectory while taking into account the geometry of the target object. Most grasp detection algorithms directly predict grasp poses in a monolithic fashion, which does not capture the composability of the environment. In this paper, we introduce an end-to-end architecture for object-centric grasping. The method uses pointcloud data from a single arbitrary viewing direction as an input and generates an instance-centric representation for each partially observed object in the scene. This representation is further used for object reconstruction and grasp detection in cluttered table-top scenes. We show the effectiveness of the proposed method by extensively evaluating it against state-of-the-art methods on synthetic datasets, indicating superior performance for grasping and reconstruction. Additionally, we demonstrate real-world applicability by decluttering scenes with varying numbers of objects. Videos and Code ***.

关键词： Geometry Grasping Computer architecture Trajectory Collision avoidance Task analysis detection algorithms

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Embedded Bit-Stream Region-of-Interest Coding of Point Cloud Attributes

Embedded Bit-Stream Region-of-Interest Coding of Point Cloud...

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IEEE Workshop on Multimedia Signal Processing

作者： Victor F. Figueiredo Ricardo L. de Queiroz Electrical Engineering Department University of Brasilia Brasilia Brazil Department of Computer Science University of Brasilia Brasilia Brazil

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

This paper presents a novel approach to embedded Region-of-Interest (ROI) attribute coding for point clouds. The proposed method is an embedded point cloud attribute coder incorporating regions of interest. The method chosen was to interleave bit-streams for ROI and non-ROI regions, which brings us some advantages. Firstly, it is independent of any specific ROI detection algorithm. Secondly, no increased complexity is imposed on the decoder. The results demonstrate an improvement in the quality of reconstructed voxels within the ROI, accompanied by some degradation of reconstructed voxels outside the ROI.

关键词： Point cloud compression Degradation Codecs Conferences Signal processing Encoding Decoding Complexity theory detection algorithms

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A Swarm Intelligence Based Community detection Algorithm in Social Networks

A Swarm Intelligence Based Community Detection Algorithm in ...

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Artificial Intelligence and Speech Technology (AIST), International Conference on

作者： Deepjyoti Choudhury Tapodhir Acharjee Department of CSE Assam University Silchar India

A community consists of a group of dense intra-connected and sparse inter-connected actors. To detect communities in social networks have earned popularity in past few years. We have concentrated on swarm intelligence based community detection in real world networks elaborated in this paper. In computational field, there are several algorithms so far suggested to detect communities in social networks. But most of the existing algorithms take the high running time and less efficient. So, there is a need of an efficient community detection algorithm. Here, a swarm intelligence based proposed method has been elucidated to detect the communities in social networks. We have evaluated algorithm on two measures namely accuracy and Normalized Mutual Information and found that the proposed approach provides efficient results than the Girvan-Newman algorithm. Our proposed method achieves highest accuracy for College Football Network as 84% and performs better in other networks too. Also, 86% NMI score as the highest one is obtained by our proposed method for College Football Network.

关键词： Social networking (online) Particle swarm optimization detection algorithms Artificial intelligence Mutual information Sports

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Survey on Effective Deep Learning-based Automated Epileptic Seizure detection

Survey on Effective Deep Learning-based Automated Epileptic ...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： R. Manjupriya A.Anny Leema School of Information technology &Engineering VIT University Vellore Tamil Nadu India

ISBN: (纸本)9781665489638

Uncontrolled neuron activity in the brain is the source of the neurological disorder known as epilepsy seizures that substantially reduce patients' quality of life and frequently risk their lives. Research on automatic seizure identification is essential for the field of seizure treatment and is necessary for seizure prevention. For these applications to be successful, the classification and detection algorithms must be accurate. Numerous methods have been studied, developed, and introduced over time. This review describes a variety of seizure detection techniques from the last six years, including both established methods and the latest deep learning techniques. Performance comparisons between the advantages and disadvantages of each method under study were made. This survey revealed that the recent research has mainly examined the strength of Deep Learning methods in identifying and categorizing seizures, which have been employed in a variety of fields such as image processing, signal processing, etc. Deep learning hybrid methods has also been the subject of studies. Finally, the research study is concluded by discussing potential future research directions.

关键词： Deep learning Neurological diseases Image processing Neurons Epilepsy Signal processing detection algorithms

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Comorbidity Disease Identification of Diabetes Insipidus Using Graph Network algorithms

Comorbidity Disease Identification of Diabetes Insipidus Usi...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Angga Aditya Permana David Agustriawan Wirawan Istiono Analekta Tiara Perdana Yaman Khaeruzzaman Reynard Matthew Yaputra Hans Philemon Limaza Gilbert Evan Tilung Sharone Angelica Jovans Farel Arden Jani Department of Informatic Faculty Engineering and Informatics Multimedia Nusantara University Tangerang Banten Indonesia Department of Biology Faculty of Science Islam Negeri SMH University Serang Banten Indonesia

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

Diabetes insipidus (DI) is a one of chronic disease and with its comorbid relationship causes a serious health problem. Therefore, this study aimed to explore the effect of the known and potential comorbidity disease with DI for a better prevention and treatment strategy, moreover, compare the performance of community detection algorithms for comorbidity detection of DI. The data was collected from pubtator3 downloaded through this link: https://***'2oy/research/pubtator3/ using this following keyword “comorbid diabetes insipidus”. Then, preprocessing steps such as remove duplicate, format the data, remove N/A value from disease information, cleaning the dataset was performed. Moreover, disease ontology identifier (DOID) labelling was conducted. Furthermore, computation or modelling for network formation, community detection and comorbidity discovery were performed. All the analysis was performed using python, R and SPARQL. From 1,074 initial articles on “comorbid diabetes insipidus” 820 unique articles were identified, leading to 3,042 unique disease names mapped to 1,108 DOIDs, refined to 578 unique DOIDs. A network with 550 nodes and 2,425 edges was formed, using a 0.5 threshold. The Louvain algorithm, favored for community detection, identified 16 key comorbidities like vascular, autoimmune, and lung diseases. The consensus among centrality algorithms highlighted these comorbidity associations, with the Louvain algorithm most effective in detecting significant communities.

关键词： Computational modeling Pulmonary diseases Prevention and mitigation Image edge detection Network analyzers Ontologies Diabetes Labeling detection algorithms Diseases

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A Point Cloud Gridding Method for Detecting Rail Intruding Foreign Object

A Point Cloud Gridding Method for Detecting Rail Intruding F...

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Prognostics and System Health Management Conference (PHM-Qingdao)

作者： Shaowei Xu Wei Liu Yang Yang Xiaozhao Jin Jin He Chengdu Jiaoda Guangmang Technology Co. Ltd Chendu China Chengdu SinoRail Electronics Co. Ltd Chendu China Chengdu Aerospace Science and Industry Big Data Research Institute Co. Ltd Chendu China School of Automation Chengdu University of Information Technology Chendu China

ISBN: (数字)9798350354010

ISBN: (纸本)9798350354027

Intruding foreign objects (IFO) pose a serious threat to railway safety operations. Existing detection algorithms often suffer from significant false alarms and missed detections, rendering them ineffective in practical IFO detection. To address this issue, we propose a point cloud gridding method for detecting rail IFO. Firstly, this method provides effective IFO detection hardware by selecting an appropriate lidar angular resolution based on the requirements for detecting IFO. Secondly, we map pillar point clouds to Bird's Eye View (BEV) to form detection grids, enabling effective IFO detection and improving detection accuracy and recall rates. Extensive experiments and real-world applications have demonstrated the effectiveness of this method.

关键词： Point cloud compression Rails Meters Laser radar Railway safety Rendering (computer graphics) Rail transportation Reliability detection algorithms Prognostics and health management

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MTP: A Minimal Task Perturbation Framework for Mitigating Poisoning Attacks in Federated Learning

MTP: A Minimal Task Perturbation Framework for Mitigating Po...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Weiqiang Jiang Tianyu Su Guangtao Wang Dongqi Han China Mobile Group Co. Ltd Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (MoE) Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Federated Learning achieves privacy protection by allowing different participants to jointly train models. However, this characteristic also makes Federated Learning susceptible to data poisoning attacks. Many existing defense methods can successfully defend against various data poisoning attacks, but they are often only effective against one of the independent and identically distributed (IID) or non-independent and identically distributed (non-IID) scenarios. In this paper, we propose a novel minimal task perturbation framework named MTP for mitigating data poisoning attacks in both IID and non-IID scenarios. To efficiently identify potential attackers, MTP utilizes a weight similarity ensemble mechanism based on four distance algorithms and clusters clients according to the fused weight similarity. Moreover, MTP uses a clipped weight aggregation to minimize the impact on the performance of main task. Experimental results show that the proposed MTP outperforms state-of-the-art methods in stability and security, and maintain higher accuracy of the main task.

关键词： Privacy Computer vision Federated learning Perturbation methods Image processing Distributed databases Stability analysis Security Protection detection algorithms

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