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检索条件"主题词=Detection Algorithms"

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Research on Fire detection Algorithm Based on Improved YOLOv5

Research on Fire Detection Algorithm Based on Improved YOLOv...

引用

IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Junyi Ma Yunmeng Li Mingyuan Qin Qiang Tong Computer Science and Information Engineering Hubei Normal University Huangshi China

ISBN: (数字)9798331504946

ISBN: (纸本)9798331504953

Economic losses and casualties caused by fires have always been a thorny issue in society. In order to monitor the occurrence of fires in real time and accurately, in view of the complexity of fire scenarios and high requirements for accuracy, a fire detection algorithm based on improved YOLOv5 is proposed. Firstly, the SimAM attention mechanism is introduced into the YOLOv5 network model to improve the characteristics of the flame. The MPDIoU loss function is optimized and Soft-NMS is introduced. Finally, in order to solve the problem of poor performance of YOLOv5 for small target detection, a small target detection layer is added. The experimental results show that the accuracy of the improved YOLOV5 model is 87.9 %, which is 9.6 % higher than that of the original YOLOv5 and the performance is better than that of the current mainstream target detection model. At the same time, the improved YOLOv5 network model has better performance for small target detection, effectively improves the accuracy of fire detection, and meets the needs of actual fire detection.

关键词： YOLO Economics Visualization Accuracy Attention mechanisms Fires Real-time systems Intelligent agents detection algorithms Monitoring

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Improved YOLOv8 algorithm for student behavior detection in a smart classroom

Improved YOLOv8 algorithm for student behavior detection in ...

引用

Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE International Conference on

作者： Long He Peng Han Guangdong Provincial Engineering Research Center for Optoelectronic Instrument School of Electronic Science and Engineering South China Normal University Foshan China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

In light of the advancements in educational technology, the smart classroom has emerged as a pivotal element in the contemporary educational landscape. Among these, student behavior detection technology is of particular importance for enhancing the quality of teaching and facilitating students' personal growth. In order to address the shortcomings of limited public datasets and low detection precision, we have created the SBC Dataset and proposed a student behavior detection algorithm, CCB-YOLOv8, which builds upon the YOLOv8 framework. Firstly, Coordinate Attention mechanism is introduced in the YOLOv8 backbone network to enhance the model's spatial perception. Secondly, the original UpSample operator is replaced by the CARAFE operator to augment the model's ability to capture key features. Finally, we introduce BiFPN in the Concat module to improve the precision and generalisation ability of the system. In comparison to YOLOv8, the precision of CCB-YOLOv8 has been enhanced by 4.3%, the recall by 5.6%, and the mAP@0.50 and mAP@0.50:0.95 by 4.4% and 5.5%, respectively. These results demonstrate that the improved algorithm exhibits heightened accuracy and robustness.

关键词： Adaptation models Accuracy Computational modeling Streaming media Robustness Real-time systems detection algorithms Optimization Robots Testing

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Classroom Automation System Using Human detection Based on YOLO Algorithm and IoT Technology

Classroom Automation System Using Human Detection Based on Y...

引用

Electrical and Information Technology (IEIT), International Conference on

作者： Mazaya Muhammad T. S. El Syaff Vincentius Jason Samhadi Brahmantya Aji Pramudita School of Electrical Engineering Telkom University Bandung Indonesia The University Center of Excellence for Intelligent Sensing-IoT Telkom University Bandung Indonesia

ISBN: (数字)9798331516888

ISBN: (纸本)9798331516895

Classroom is an important facility in the school or university to be utilized as a learning space and lessons happen. Students and teachers meet in the classroom on a daily, with different schedules. Hence, every class needs to be monitored and controlled for finding out the class conditions. However, the staffs have to monitor the classroom by checking the classroom one by one. Thus, it is not efficient and increase the workload if there are many classes to be checked. Therefore, automated technology can be implemented to reduces the workload and increases staff efficiency when controlling and monitoring the classroom, especially in the context of time. In this study, an IoT-based system was proposed as a solution to control the classroom condition. This system used class schedules and human presence as a parameter to control the classroom. A camera utilized to detect the object and performed YOLOv8 algorithm to analyze the condition. in this system, firebase was leveraged to store every data from detecting process. Then, the data was used for controlling the actuators, such as door locking, lamp, and fan, which were controlled by ESP32. The result showed that the proposed system could perform with accuracy of 57%, sensitivity of 67%, and positive predictive value of 79%. The proposed system can achieve a success rate of 90% with less than 20 second of time delay which makes this system time effective. But it is not yet known whether this system will work effectively in low light conditions.

关键词： YOLO Schedules Fans Accuracy Sensitivity Prediction algorithms Data transfer Information technology detection algorithms Monitoring

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Research on obstacle detection algorithm based on object recognition

Research on obstacle detection algorithm based on object rec...

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Electrical, Automation and Computer Engineering (ICEACE), International Conference on

作者： Lu Bian Weimin Xia Yuanpeng Tan Miao Zhao State Grid Corporation Limited Beijing China State Grid Zhongxing Property Beijing Co. LTD Beijing China China Electric Power Research Institute Limited Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This paper delves into an obstacle detection algorithm that utilizes object recognition to enhance the precision of identifying small objects. By extending the YOLOv5s model with an additional layer dedicated to detecting small targets, we achieve multi-scale detection capabilities, thereby bolstering the recognition prowess for small obstacle targets. At the same time, the deformable convolution DCNv2 is combined to make full use of its ability to capture the features of small targets with large shape changes. To address the issue of redundant bounding boxes, we incorporate the Soft-NMS algorithm, which proficiently mitigates the inadvertent removal of bounding boxes, thereby enhancing both the accuracy and trustworthiness of the detection outcomes. Experimental findings underscore the remarkable performance improvement of the proposed algorithm, particularly in tasks involving the detection of small targets.

关键词： YOLO Automation Accuracy Target recognition Shape Convolution detection algorithms

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YOLO-DC: Enhancing object detection with deformable convolutions and contextual mechanism

YOLO-DC: Enhancing object detection with deformable convolut...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Dengyong Zhang Chuanzhen Xu Jiaxin Chen Bin Deng Xin Liao School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Hydraulic and Environmental Engineering Changsha University of Science and Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Existing object detection methods often optimize model structure, loss functions, and data preprocessing, but enhancements via convolutional techniques are often overlooked. Additionally, increasing network depth leads to the loss of key features. This paper introduces YOLO-DC, which enhances object detection with deformable convolution and contextual mechanisms. YOLO-DC includes a Deformable Convolutional Module (DCM) and a Contextual Fusion Downsampling Module (CFD). The DCM module uses a modified deformable convolution of Multiscale Spatial Channel Attention (MSCA) to extend the receptive field and improve feature extraction. The CFD integrates contextual and local features post-downsampling, reducing information loss. Compared to YOLOv8-N, YOLO-DC-N improves average precision (AP) by 3.5%, reaching 40.8% on the Microsoft COCO 2017 dataset, with similar FPS and inference time. The model demonstrates superior performance compared to other state-of-the-art detection algorithms. The source code is available at: https://***/Object-detection-01/***.

关键词： Convolutional codes Deformable models Convolution Source coding Data preprocessing Object detection Information processing Feature extraction Data models detection algorithms

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Community detection Algorithm Based On High-Order Variational Graph Autoencoder

Community Detection Algorithm Based On High-Order Variationa...

引用

Image Processing (ASIP), Asia Symposium on

作者： Yiwen Shen Xiaoyan Zheng Ran Yang School of Information Technology and Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9798350376159

ISBN: (纸本)9798350376166

The topic of community discovery algorithm has important research significance in complex network scientific research, and the acquisition of network topology structure information is one of its key research areas. With the exponential growth of community network size, there is not only the problem of dimensionality disaster, but also the difficulty of extracting sufficient community structure information. Obviously, the experimental performance of traditional community discovery algorithms is difficult to meet our needs in such an environment. At the same time, most existing models only use traditional adjacency matrices to obtain topological structure information. In response to the above problems, this paper proposes a community discovery algorithm (Aggregation-VGAE) based on variational graph autoencoders to obtain high-order topological structure information, extracting more accurate high-order topological structure information to find community structures more accurately. First, an aggregation matrix is generated by extracting the first-order and second-order structural information of the topological graph; secondly, a variational graph autoencoder is used to extract and reduce the dimensionality of the feature matrix containing network attributes to obtain a low-dimensional embedding matrix; finally, K- means clustering algorithm to obtain community structure information. We compare Aggregation-VGAE with the baseline model VGAE and its 7 other algorithms using the NMI evaluation metric on the citation network dataset. By evaluating the algorithm on a large real data set, the experimental results and visualization of the clustering results show that the Aggregation-VGAE algorithm has better community segmentation results in the citation network than other various algorithms.

关键词： Measurement Dimensionality reduction Disasters Clustering algorithms Data visualization Complex networks Feature extraction Robustness Data mining detection algorithms

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Cross-Layer Cascading Chain-of-Thought Based Object detection Algorithm for Aerial Images in Foggy Weather

Cross-Layer Cascading Chain-of-Thought Based Object Detectio...

引用

International Conference on Big Data and Information Analytics (BigDIA)

作者： Xuanyu Zhang Jian Huang Sihang Zhou Dexing Wei Changyu Qian Zexin Li College of Intelligence Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

In response to the challenges posed by fog obstruction and small object in the detection of aerial images under foggy conditions, this paper presents an image restoration network (CLC-CPA) based on the cross-layer cascading chain-of-thought prompt mechanism that can be integrated with detection algorithms. The proposed network executes the restoration strategy step by step, with each step based on the results of the previous step (i.e., chain-of-thought mode), gradually restoring foggy images to a level conducive to detection. Additionally, it combines deep and shallow information via cross-layer cascading to preserve the features of small objects. Furthermore, the HorNet is employed to enhance the backbone network of the detection algorithm, extending feature extraction into higher-order spaces, which enables the network to capture more distinguishable spatial relationships and improves the detection accuracy of small objects in aerial images. Finally, utilizing the VisDrone dataset, a foggy scene aerial image dataset (FoggyVisDrone) is constructed based on the atmospheric scattering model. Experimental results indicate that, in the test set of aerial images with varying fog concentrations, the mAP50 of the improved algorithm exceeds that of the baseline algorithm by more than 4.4%.

关键词： Cross layer design Accuracy Atmospheric modeling Scattering Object detection Big Data Feature extraction Image restoration detection algorithms Meteorology

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ELAA Near-Field Localization and Sensing with Partial Blockage detection

ELAA Near-Field Localization and Sensing with Partial Blocka...

引用

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Hui Chen Pinjun Zheng Yu Ge Ahmed Elzanaty Jiguang He Tareq Y. Al-Naffouri Henk Wymeersch Chalmers University of Technology Sweden King Abdullah University of Science and Technology KSA University of Surrey UK Technology Innovation Institute UAE

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

High-frequency communication systems bring extremely large aperture arrays (ELAA) and large bandwidths, integrating localization and (bi-static) sensing functions without extra infrastructure. Such systems are likely to operate in the near-field (NF), where the performance of localization and sensing is degraded if a simplified far-field channel model is considered. However, when taking advantage of the additional geometry information in the NF, e.g., the encapsulated information in the wavefront, localization and sensing performance can be improved. In this work, we formulate a joint synchronization, localization, and sensing problem in the NF. Considering the array size could be much larger than an obstacle, the effect of partial blockage (i.e., a portion of antennas are blocked) is investigated, and a blockage detection algorithm is proposed. The simulation results show that blockage greatly impacts performance for certain positions, and the proposed blockage detection algorithm can mitigate this impact by identifying the blocked antennas.

关键词： Location awareness Three-dimensional displays Simulation Apertures Sensors Noise measurement Synchronization detection algorithms Uplink Antenna arrays

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Workflow Development of AI Based Spectrogram Analysis with Real-Time Out of Distribution detection

Workflow Development of AI Based Spectrogram Analysis with R...

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International Carpathian Control Conference (ICCC)

作者： Lóránt Szabó Zoltán Weltsch John von Neumann University AI Research Center Kecskemét Hungary University of Györ Györ Huugary

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

The aim of this paper is to investigate possible workflows for OOD pattern recognition in AI-based spectrogram analysis, applied in industrial manufacturing environment. First, we attempt to identify and articulate the challenges associated with OOD recognition in the context of spectrogram analysis, where the acoustic sources are subtle and often complex signals. These deserve particular attention, since the effectivity of OOD detection algorithms are acceptable in case of significant deviations, however, it is questionable for fine anomalies. In addition, it is also discussed here, how OOD records can affect the accuracy and reliability of AI models in terms of equipment failure identification and process inefficiencies. Last, methodes are proposed for OOD-pattern recognition. The integrability of these methods into existing manufacturing workflows in terms of practicality, adaptability and effectiveness are also investigated.

关键词： Training Adaptation models Real-time systems Manufacturing Pattern recognition Reliability detection algorithms

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Covariance-Based Activity detection Algorithm in Clustered Cell-Free Massive MIMO

Covariance-Based Activity Detection Algorithm in Clustered C...

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Computer and Information Processing Technology (ISCIPT), International Symposium on

作者： Haowen Ye Ranran Lv Kai Sun Wei Huang School of Electronic Information Engineering Inner Mongolia University Hohhot China

ISBN: (数字)9798350388404

ISBN: (纸本)9798350388411

Massive Machine-Type Communications (mMTC) plays an increasingly important role in future wireless communications, and the use of a large number of low-power devices in conjunction with cell-free massive MIMO networks is a promising enabling technology. In this research, we explore activity detection for Grant-Free Random Access in massive mMTC within a cell-free Massive MIMO network employing distributed antenna arrays. Each active device sends a non-orthogonal pilot sequence to geographically clustered Access Points (APs) using the K-means algorithm. The clustered APs then relay the received signals to the Central Processing Unit (CPU) for collaborative activity detection. Simulation results demonstrate that for extensive coverage areas, the covariance-based activity detection algorithm with K-means clustered APs exhibits reduced complexity and improved activity detection efficiency while maintaining robust performance.

关键词： Wireless communication Simulation Partial transmit sequences Clustering algorithms Massive MIMO Central Processing Unit detection algorithms

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