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检索条件"主题词=detection algorithms"

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Personnel detection Algorithm Based on Spatiotemporal Mapping Event Frames

Personnel Detection Algorithm Based on Spatiotemporal Mappin...

引用

2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Jiannan Zhao Lei Zhao Wenyuan Zhang Zhiteng Li Juyi Wu Feng Shuang Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment School of Electrical Engineering Guangxi University Nanning China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

The personnel detection algorithms based on frame-based traditional cameras exhibit low accuracy in harsh lighting conditions or rapidly moving scenes. To address this issue, we propose a personnel detection network called YOLO-SMCA, which utilizes an event camera to capture personnel motion information based on Spatiotemporal Mapping Event Frames. By incorporating temporal information into the event mapping method to generate event frames, we overcome the limitations of traditional event representation methods, which often lead to the loss of temporal information. This enhancement significantly improves the feature representation capability of the input events. Building on this foundation, we employ the YOLOv5n neural network for personnel detection using spatiotemporal mapping event frames. Additionally, we embed the Coordinate Attention (CA) mechanism in the head of the network to enhance the model’s focus on key features during final decision-making. Through comparative and visualization experiments conducted on the PEDRo dataset, the YOLO-SMCA achieved a mean Average Precision (mAP) of 98.1%, outperforming other methods and meeting the accuracy requirements for personnel detection in complex environments.

关键词： Accuracy Face recognition Buildings Cameras Video surveillance Robustness Spatiotemporal phenomena Personnel detection algorithms Testing

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DFT-Net: A Bimodal Object detection Algorithm for Complex Traffic Environments

DFT-Net: A Bimodal Object Detection Algorithm for Complex Tr...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Jing Lian Yibin Zhang Haoyu Li Jun Hu Linhui Li School of Mechanical Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In response to the challenge of single-modal sensors struggling to rapidly and accurately detect in complex traffic environments, a DFT-Net detection algorithm based on the fusion of visible light and infrared bimodal features is proposed. Firstly, the algorithm constructs a dual modal feature extraction network and designs an efficient aggregation module with stronger feature extraction capability. Secondly, the algorithm utilizes a framework of self-attention and cross-attention mechanisms to fuse the features of visible light and infrared modalities. This framework facilitates information interaction between different modalities through query-guided mechanisms. To reduce the computational load of the dual modal feature transformer module, a spatial feature compression module is introduced. Finally, in order to mitigate visible noise and low infrared resolution, this paper combines Transformer and CBAM attention mechanism. The designed algorithm is compared with various detection algorithms on multiple datasets, and the results demonstrate its effectiveness. On the FLIR-gan dataset, compared to the YOLOv7 detection algorithms for visible light and infrared, our DFT-Net achieved an accuracy improvement of 26.8% and 26.7% respectively, and 2.8% improvement compared to the bimodal detection algorithm CFT. This indicates that the algorithm exhibits good detection performance in complex traffic environments.

关键词： Fuses Noise Object detection Sensor phenomena and characterization Sensor fusion Feature extraction Transformers detection algorithms Spatial resolution Informatics

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Road Crack detection Algorithm Based on Large Convolution Kernels

Road Crack Detection Algorithm Based on Large Convolution Ke...

引用

International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： Yunxiang Liu Bin Nan Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

Road crack detection is widely used in road maintenance and bridge safety monitoring. The complex textures of road cracks lead to occlusion between targets, and environmental factors affect images, causing color shifts and blurring. To address these issues, this paper proposes the YOLOv8-LSK road crack detection algorithm. First, a Spatial Fusion Module (SMF) is designed to enhance the focus on spatial dimension information, improving the detection capability of occluded cracks. Second, a large-kernel convolution attention feature extraction module is designed to enhance the feature extraction capability of blurred and color-shifted road images. Finally, structural reparameterization (SR) is applied to the cross-scale feature fusion unit of YOLOv8-LSK, optimizing the model’s detection speed. Experimental results show that the YOLOv8-LSK algorithm achieves a detection accuracy of $\mathbf{7 2. 1 \%}$ on the RDD2022 dataset, with detection speeds of 33FPS, respectively.

关键词： Convolution Image color analysis Roads Feature extraction Safety Maintenance detection algorithms Kernel Informatics Monitoring

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Discriminative Community detection for Multiplex Networks

Discriminative Community Detection for Multiplex Networks

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IEEE Workshop on Machine Learning for Signal Processing

作者： Meiby Ortiz-Bouza Selin Aviyente Department of Electrical and Computer Engineering Michigan State University

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

Multiplex networks have emerged as a promising approach for modeling complex systems, where each layer represents a different mode of interaction among entities of the same type. A core task in analyzing these networks is to identify the community structure for a better understanding of the overall functioning of the network. While different methods have been proposed to detect the community structure of multiplex networks, the majority deal with extracting the consensus community structure across layers. In this paper, we address the community detection problem across two closely related multiplex networks. For example in neuroimaging studies, it is common to have multiple multiplex brain networks where each layer corresponds to an individual and each group to different experimental conditions. In this setting, one may be interested in both learning the community structure representing each experimental condition and the discriminative community structure between two groups. In this paper, we introduce two discriminative community detection algorithms based on spectral clustering. The first approach aims to identify the discriminative subgraph structure between the groups, while the second one learns the discriminative and the consensus community structures, simultaneously. The proposed approaches are evaluated on both simulated and real world multiplex networks.

关键词： Multiplexing Neuroimaging Accuracy Conferences Machine learning Signal processing detection algorithms Complex systems

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A Novel Reinforcement Learning Multi-Objective Community detection Algorithm with $\epsilon$-Gradient-Greedy Strategy

A Novel Reinforcement Learning Multi-Objective Community Det...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Wenhong Wei Yi Meng Qingxia Li School of Computer Science and Technology Dongguan University of Technology Dongguan China School of Artificial Intelligence Dongguan City University Dongguan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Accurately categorizing communities within a social network is a crucial aspect of community detection, carrying significant practical relevance. To achieve a higher quality of community division, we combine reinforcement learning methods to learn the distribution characteristics of community nodes during the iteration process of the algorithm, which guides the nodes to migrate to other communities and enhances the algorithm's global search capability. Through a new $\epsilon$ -gradient-greedy strategy, which can obtain a node's gradient information relative to its neighbors and achieve higher performance in local search. To speed up the adaptability of the algorithm at the beginning of the iteration and to alleviate the resolution limitation imposed by modularity optimization, this paper employs a triangular subnetwork-based weight assignment method for balancing the weights of each edge in the network. Experimental results on real-world and synthetic network datasets demonstrate that our method's community identification precision outperforms recent community detection algorithms, exhibiting higher accuracy, higher resolution, and adaptability to various network characteristics and structural changes.

关键词： Accuracy Q-learning Social networking (online) Image edge detection Benchmark testing detection algorithms Optimization Cybernetics

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Improved YOLOv8 algorithm for student behavior detection in a smart classroom

Improved YOLOv8 algorithm for student behavior detection in ...

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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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Improved Cassava Plant Disease Classification with Leaf detection

Improved Cassava Plant Disease Classification with Leaf Dete...

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

作者： Ming Xuan Chai Yao Deng Fam Quinito Norman Octaviano Chih-Yang Pee Lai-Kuan Wong Mas Ira Syafila Mohd Hilmi Tan John See Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia School of Mathematical and Computer Science Heriot-Watt University (Malaysia) Putrajaya Malaysia

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Cassava (Manihot esculenta Crantz), a crucial food source for millions, has experienced significant crop yield losses due to various plant diseases. To address this issue, extensive research has been conducted on the Cassava 2020 dataset released by the Makerere AI Lab. Images in this dataset are captured in complex real-world environments, adding difficulty to the classification process. While several deep learning models have shown promising results, we believe that reducing interference from overlapping leaves and complex backgrounds could further enhance classification performance. To accomplish this, up to five leaves of interest have been identified and annotated in each image. These annotated leaves are then used to train a detection model: Faster R-CNN with ResNet-101, designed to automatically detect leaves of interest in the images. The identified leaves are separated from the background through a masking process, producing masked images (Mi), where i indicates the maximum number of leaves in the masked images, with i ranging from 1 to 5. The masked images M1 to M5 are then trained on various CNN models; i.e. EfficientNetB1, DenseNet121, ResNet50, and Xception, for Cassava disease classification. The results show that classifiers trained with M3 improve the accuracy by 2.13% to 3.06% compared to models trained with original images. The main contribution of this work is a novel technique capable of identifying leaves of interest from complex backgrounds to produce masked images, which improve the accuracy of various CNN-based classification models, and a novel leaf annotation where up to 5 leaves of interest have been annotated for each image in Cassava 2020 dataset. Annotated images can serve as a valuable resource for advanced cassava disease analysis, detection, and classification.

关键词： Training Plant diseases Accuracy Annotations Interference Information processing Feature extraction detection algorithms Faces Residual neural networks

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Eye State detection Algorithm Based on Shallow Dual-scale Siamese CNN for manufacturing workshop workers

Eye State Detection Algorithm Based on Shallow Dual-scale Si...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Xiaowen Chen Qingsheng Xie Ruifeng Li Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang China School of Digital Media Guiyang Institute of Information Science and Technology Guiyang China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Eye State detection (ESD) is an important means of safety risk detection during the interaction between humans and computers in a workshop setting. The deep network model incurs a significant computational cost, making it unsuitable for deployment on computing devices that have weak computing power. This work created a shallow dual-scale Siamese-based ESD model. First, the shallow dual-scale Siamese network is used to extract eye image features, and the high-dimensional features of the Siamese channels are subjected to feature fusion and principal component analysis. Finally, this network is inputted into the Support Vector Machine(SVM) for training and testing. Experiments on the Open and Close Eyes(OACE) and Human Eyes(HE) public datasets show that the detection speed and detection performance comprehensive indicators of the detection algorithm are better than the other seven deep baseline models, and it has better robustness on color and grayscale images with different light intensities and color depths.

关键词： Training Image color analysis Conferences Computational modeling Feature extraction Electrostatic discharges Robustness Vectors Manufacturing detection algorithms

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Disease detection Algorithm of Concrete Bridges Based on Improved YOLOv7

Disease Detection Algorithm of Concrete Bridges Based on Imp...

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Natural Language Processing (ICNLP), International Conference on

作者： BoWen Song Jing Gu School of Electronic Engineering Xi'an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Aiming at the problem that the disease detection algorithm of concrete bridges based on machine learning and deep learning is not high in recognition accuracy, this paper proposes an efficient three-dimensional coordinate attention mechanism (ETCA) based on the YOLOv7 network, which uses this structure to pay attention weighting to the feature information on feature maps of different sizes in the feature fusion network and fully extract effective features. At the same time, the Anchor Free detection algorithm is introduced, and a positive and negative sample allocation strategy without anchor frame is proposed, so that the model is no longer limited by the size and aspect ratio of the preset box, but directly generates the prediction results through the prediction box regression parameters of the network, which not only enhances the generalization ability of the model for diseases of different sizes in complex backgrounds, but also reduces the complexity of the model.

关键词： Bridges Object detection Predictive models Feature extraction Natural language processing Hardware Data mining Resource management detection algorithms Diseases

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An Enhanced Deadlock detection Methodology Using Tarjan's Algorithm

An Enhanced Deadlock Detection Methodology Using Tarjan's Al...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Adhi Neeraja Chimakurthy Mounika Begum Kamalapuram Vigneswara Reddy Rebbavarapu Henry Koushal Divya K. V Dept. of Computer Science Engineering Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

The reliability and performance of concurrency are severely affected by deadlocks in concurrent systems. An efficient deadlock detection algorithm is needed to ensure that any deadlock cases can be pre-empted. The paper introduces a novel algorithm based on Tarjan's method for detecting deadlocks. The algorithm identifies strongly connected components in a directed graph and accurately tracks dependencies among processes using a wait-for graph, enabling precise deadlock detection in concurrent systems. It's implementation is described hereby and its performance is evaluated through extensive experiments on various scenarios. Therefore, this re- search has shown how important it is to detect deadlocks in these concurrent systems which improves their stability as well as reliability. The results demonstrates that the proposed deadlock detection algorithm performs its task efficiently, which is crucial for enhancing the consistency and stability of concurrent systems. This work makes a significant contribution to the development of deadlock detection techniques and offers valuable insights into their application. It is aimed at preventing deadlocks in real-world scenarios.

关键词： Concurrent computing Directed graphs System recovery Reliability detection algorithms Information technology

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