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检索条件"主题词=Detection Algorithms"

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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...

引用

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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An Enhanced Deadlock detection Methodology Using Tarjan's Algorithm

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

引用

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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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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Research on Wheat Ear detection Algorithm Based on Improved YOLOv8

Research on Wheat Ear Detection Algorithm Based on Improved ...

引用

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

作者： Chunyu Zhang Sitong Liu Zeran Hao Datian Niu School of Science Dalian Minzu University Liaoning China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Deep learning techniques are of significant importance in the detection of wheat ears in the field of smart agriculture, bringing innovation and improvement to the agricultural sector. How to accurately detect wheat ears has become a challenging task due to the high density, diversity, and complex growth cycle of wheat ears, as well as the complex background of the detection process, which may result in false positives, false negatives, and low detection rates. In this paper, a novel wheat spike detection technique is proposed, which is optimized on the basis of YOLOv8. First, RepNCSPELAN4 is introduced into the backbone network to replace the original C2F convolution, which enhances the feature extraction ability of the model. Second, a new MSAM module is introduced into the Bottleneck of the neck C2f module to construct a new C2fMSAM module; finally, the original loss function is replaced by the normalized Wasserstein distance loss. The experimental results show that the accuracy, recall and average accuracy of the improved YOLOv8 model are improved by 1.1%, 3.5% and 2%, respectively, compared with the baseline model.

关键词： Smart agriculture Deep learning Technological innovation Accuracy Biological system modeling Computational modeling Ear Object detection Magnetic heads detection algorithms

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A Review of Lane Line detection Research

A Review of Lane Line Detection Research

引用

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

作者： Hao Wang Yuyang Gu Shanghai Institute of Technology SIT Shanghai China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

Lane line detection is an important technology in the field of intelligent driving system, which has a significant impact on the development of intelligent driving system. This paper reviews the research progress of lane line detection algorithms, describes the process of traditional lane line detection algorithms and deep learning based lane line detection algorithms respectively, and elaborates the representative implementation methods. At the same time, this paper also describes in detail some public datasets commonly used for lane line detection tasks. Finally, some existing problems of lane line detection are summarized, and the development direction of lane line detection research is prospected.

关键词： Deep learning Reviews Training data Focusing detection algorithms Informatics Meteorology

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Fire detection algorithm with multi-scale feature focus and diffusion

Fire detection algorithm with multi-scale feature focus and ...

引用

Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Qi Xiangming Zhang Yiran School of Software Engineering Liaoning Technical University Huludao China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

Flame and smoke detection in fire detection systems can suffer from low accuracy and high false detection rates due to background and environmental factors. To address this, a multi-scale feature focusing and diffusion multilayer fusion fire detection algorithm is proposed, using YOLOv8s as the baseline model. This approach introduces large separable convolutional kernel attention at the end of the backbone network to expand the receptive field, enhance the understanding of spatial feature relationships, and improve feature extraction capabilities. The neck network is enhanced by refining the PANet structure, adding a feature focusing module for multi-scale feature fusion, and incorporating a feature diffusion mechanism to boost the model's utilization of global context information and multi-layer feature integration. The detection head is upgraded with a dynamic detection head featuring unified attention, which consolidates three types of attention to improve detection accuracy. After algorithm optimization and testing, the improved model shows a 1.4 percentage point increase in mAP@0.5, a 2.6 percentage point increase in mAP@0.5:0.95, and a detection speed of 84 fps, outperforming other mainstream object detection algorithms.

关键词： Accuracy Scientific computing Heuristic algorithms Refining Fires Focusing Feature extraction detection algorithms Optimization Testing

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Terminal Abnormal State Recognition Method Based on Change Point detection of Sequence

Terminal Abnormal State Recognition Method Based on Change P...

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Energy Engineering and Power Systems (EEPS), International Conference on

作者： Lingji Kong Xiaoguang Yi Huadong Zhang Wenxiu Shi Xu Meng Heze Power Supply Company State Grid Shandong Electric Power Company Heze China

ISBN: (数字)9798350366914

ISBN: (纸本)9798350366921

The detection of terminal abnormal state is very important to improve the safety and efficiency of power system. A method was proposed in this article based on sequence change point detection to detect and recognize the abnormal state. Aiming at the characteristics that the level of electricity consumption may increase or decrease, the bilateral CUSUM algorithm based on sliding window was applied in this research. Experiments showed that this method could identify the abnormal state accurately and had high detection accuracy, which provided strong support for the safe operation of power detection terminal.

关键词： Accuracy Electricity Data acquisition Power systems Safety detection algorithms

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Research on GNSS spoofing detection method based on tracking loop moving average processing

Research on GNSS spoofing detection method based on tracking...

引用

Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Xingjun Su Shaoxiong Li Yong Hu Shen Zhao Chinese Flight Test Establishment Xi’an China AVIC The First Aircraft Institute Xi’an China School of Intelligent Engineering and Intelligent Manufacturing Hunan University of Technology and Business China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

A spoofing detection algorithm based on Tracking Loop Correlation Output (TLCO) is proposed to address the issues of limited applicability and significant fluctuations in detection performance of traditional detection algorithms. To suppress noise, the TLCO Moving Average (TLCO-MA) detection algorithm is further proposed. The construction of TCCO-MA uses the output of correlators to multiply and linearly weight the TLCO detection metric, and uses skewness and kurtosis to jointly verify its approximate normality. Then, the TLCO is subjected to MA filtering to obtain the TLCO-MA detection metric. Conduct spoofing detection experiments using DS2, DS3, and DS5 from the TEXBAT database of the University of Texas, and analyze the detection performance of the proposed algorithm and traditional SQM algorithms in multiple scenarios, including detection probability, sensitivity, immediacy, detection range, and robustness. The experimental results show that compared with the traditional SQM algorithm, TLCO-MA has better detection performance and a wider range of applicable scenarios.

关键词： Tracking loops Fluctuations Sensitivity Filtering Databases Noise Approximation algorithms Kurtosis Robustness detection algorithms

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Multi-Scale Feature Fusion Module for Change detection in Remote Sensing Images

Multi-Scale Feature Fusion Module for Change Detection in Re...

引用

Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Parameshachari B. D Laith H. Alzubaidi Sujata N Patil Hemalatha K. L Manaswini Pradhan Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University of Babylon Babylon Iraq Department of Radiation Oncology Thomas Jefferson University Philadelphia USA Department of Information Science and Engineering Sri Krishna Institute of Technology Bangalore India Department of Computer Science Fakir Mohan University Odisha India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Change detection refers to an identification of pixels that have undergone semantic changes among two remote sensing images captured at different times in a same area. The problem statement is that remote sensing images has high-resolution causes a challenge for current deep learning-based change and missed detection of small targets. It is overcome by the proposed method with Multi-scale Feature Fusion (MSFF) for feature fusion; semantic variation data is extracted from the Global Semantic Aggregation Module (GSAM); during a decoding phase, Attention Fusion Module (AFM) is applied to extract features; and Cross-scale Fusion Module (CFM) is used to enhance accuracy by removing background noise and the Levir-CD dataset is used. The numerical validation of this approach has a precision of 95.43%, a recall of 97.80%, an IoU of 93.49%, an OA of 99.67%, and a Kappa of 96.59%. The Attention-Guided Siamese Network (SAGNet), Multi-Stage Progressive Change detection Network (MSP-CD), Teacher-Student Framework (TSF), and Global Attention Module (GAM) are the existing models; the limitations of these methods are overcome by the proposed method.

关键词： Deep learning Accuracy Uncertainty Image edge detection Semantics Feature extraction Numerical models Data mining detection algorithms Remote sensing

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Towards Robust Perception for Assistive Robotics: An RGB-Event-LiDAR Dataset and Multi-Modal detection Pipeline

Towards Robust Perception for Assistive Robotics: An RGB-Eve...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Adam Scicluna Cedric Le Gentil Sheila Sutjipto Gavin Paul Robotics Institute Faculty of Engineering and Information Technology University of Technology Sydney (UTS) Australia

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

The increasing adoption of human-robot interaction presents opportunities for technology to positively impact lives, particularly those with visual impairments, through applications such as guide-dog-like assistive robotics. We present a pipeline exploring the perception and "intelligent disobedience" required by such a system. A dataset of two people moving in and out of view has been prepared to compare RGB-based and event-based multi-modal dynamic object detection using LiDAR data for 3D position localisation. Our analysis highlights challenges in accurate 3D localisation using 2D image-LiDAR fusion, indicating the need for further refinement. Compared to the performance of the frame-based detection algorithm utilised (YOLOv4), current cutting-edge event-based detection models appear limited to contextual scenarios, such as for automotive platforms. This is highlighted by weak precision and recall over varying confidence and Intersection over Union (IoU) thresholds when using frame-based detections as a ground truth. Therefore, we have publicly released this dataset to the community, containing RGB, event, point cloud and Inertial Measurement Unit (IMU) data along with ground truth poses for the two people in the scene to fill a gap in the current landscape of publicly available datasets and provide a means to assist in the development of safer and more robust algorithms in the future: https://***/revel/.

关键词： Three-dimensional displays Laser radar Accuracy Tracking Event detection Pipelines Object detection Assistive robots Vehicle dynamics detection algorithms

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