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检索条件"主题词=Detection Algorithms"

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Safety Helmet detection Using Improved YOLOv8

Safety Helmet Detection Using Improved YOLOv8

引用

Big Data & Artificial Intelligence & Software Engineering (ICBASE), International Conference on

作者： Zhao Chen Baoping Liu Xiang Gao Shaodong Sun Bin Yao State Grid Gansu Electric Power Company Gansu China Jinchang Power supply company of The State Grid Jinchang China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

The wearing of safety helmet directly affects the life safety of workers in the construction site or production workshop, and it is of great significance for the supervision of workers’ helmet wearing. In this paper, for the current helmet wearing detection algorithm in the complex environment detection accuracy is not high, put forward a yolov8-based improved helmet detection algorithm YOLOv8-Focaler-SE, in the network structure to add SE attention mechanism module, so that the network more accurately locate the region of interest in the target, at the same time in the CIOU loss function on the basis of the introduction of the Focaler-CIoU loss, thus thereby thereby improving the overall performance of the algorithm. Experimental results on the safety helmet wearing dataset show that YOLOv8-Focaler-SE improves the precision, recall, mAP 50 and mAP $50-95$ metrics by $1.2 \%$, $2.1 \%, 1.5 \%$, and $2.1 \%$, respectively, while the number of model parameters remains essentially unchanged when compared to the yolov8n algorithm. The method improves the performance of the detection algorithm, which can accurately detect helmet wearing and provide technical support for safety production.

关键词： Measurement Head Accuracy Attention mechanisms Software algorithms Production Safety Indexes detection algorithms Software engineering

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Localizing and Assessing Node Significance in Default Mode Network Using Sub-Community detection in Mild Cognitive Impairment

Localizing and Assessing Node Significance in Default Mode N...

引用

IEEE International Symposium on Biomedical Imaging

作者： Ameiy Acharya Chakka Sai Pradeep Neelam Sinha Centre for Brain Research Indian Institute of Science Bengaluru International Institute of Information Technology Bangalore

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Our study aims to utilize fMRI to identify the affected brain regions within the Default Mode Network (DMN) in subjects with Mild Cognitive Impairment (MCI), using a novel Node Significance Score (NSS). We construct subject-specific DMN graphs by employing partial correlation of Regions of Interest (ROIs) that make-up the DMN. For the DMN graph, ROIs are the nodes and edges are determined based on partial correlation. Four popular community detection algorithms (Clique Percolation Method (CPM), Louvain algorithm, Greedy Modularity and Leading Eigenvectors) are applied to determine the largest sub-community. NSS ratings are derived for each node, considering (I) frequency in the largest sub-community within a class across all subjects and (II) occurrence in the largest sub-community according to all four methods. After computing the NSS of each ROI in both healthy and MCI subjects, we quantify the score disparity to identify nodes most impacted by MCI. The results reveal a disparity exceeding 20% for 10 DMN nodes, maximally for PCC and Fusiform, showing 45.69% and 43.08% disparity. This aligns with existing medical literature, additionally providing a quantitative measure that enables the ordering of the affected ROIs. These findings offer valuable insights and could lead to treatment strategies aggressively targeting the affected nodes.

关键词： Correlation Atmospheric measurements Image edge detection Biomedical measurement Functional magnetic resonance imaging Particle measurements detection algorithms

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YOLO-Arc: An Improved Arc detection Algorithm for Spike Camera Imaging in Industrial Scenes

YOLO-Arc: An Improved Arc Detection Algorithm for Spike Came...

引用

Cognitive Engineering and Intelligent Interaction (CEII), Asia Conference on

作者： Changhao Yuan Shuaipeng Wu Kejiang Ye Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Southern University of Science and Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798331508760

ISBN: (纸本)9798331508777

In the power industry, arc detection is an important task as high-intensity or high-frequency arcs can cause serious damage to electrical equipment and threaten the reliability of power systems. The traditional cameras are difficult to capture instantaneous arc phenomena due to the limitations of frame rate and data processing. The spike camera is an ideal choice for arc capture because of its high speed and temporal resolution. However, due to the unique imaging mechanism of the spike camera, its reconstructed images have limitations in detail expression, which poses challenges to traditional object detection algorithms. Therefore, this paper proposes an improved arc detection algorithm based on YOLOv8, called YOLO-Arc. By replacing the CIoU loss function in YOLOv8 with the DIoU loss function, this method improves the performance of the model in small object detection with complex backgrounds. The experimental results show that the arc detection result of YOLO-Arc in industrial scenes is significantly better than that of YOLOv8. The mAP value on the test set is 4.9% higher than that of the baseline model, and the arc detection precision and recall rate in simulated industrial scenes are increased by 2% and 7.3%, respectively.

关键词： Image resolution Object detection Network architecture Cameras Power industry Power system reliability Reliability detection algorithms Transient analysis Image reconstruction

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A Change detection Algorithm for High Resolution Remote Sensing Images based on Data Analysis

A Change Detection Algorithm for High Resolution Remote Sens...

引用

Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Tao Liu Bohan Zhang College of Computer Science and Technology Heilongjiang Institute of Technology Harbin China School of Information Engineering China University of Geosciences Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

With the development of remote sensing technology to high resolution, high resolution remote sensing images are widely used in change detection. This paper uses Convolutional Neural Network (CNN) as its core model. The first step is data preprocessing, including data cutting, re-projection and normalization. Then the output of post-processing and change model is refined by threshold processing and morphological operation. Experiments show that the maximum detection time of CNN is only 400 milliseconds, showing higher speed performance. In terms of detection accuracy, the best CNN accuracy rate is 96.6%, and the worst CNN accuracy rate is 72.3%. The highest accuracy rates of RNN and SVM are 69.4% and 67.3%, respectively. As far as the reliability of detection is concerned, CNN has the best reliability, which is 96.9%, RNN is 79% and SVM is 72.2%. These results show that CNN can achieve better performance in typical high-resolution remote sensing image change detection tasks.

关键词： Support vector machines Accuracy Data analysis Change detection algorithms Data preprocessing Data enhancement Data models Convolutional neural networks detection algorithms Remote sensing

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Research on Improved FBD Harmonic detection Method Based on CNF-DSOGI

Research on Improved FBD Harmonic Detection Method Based on ...

引用

Power Systems and Electrical Technology (PSET), IEEE International Conference on

作者： Bo Jiang Ying Xue Cheng Pi Ziwei Zhou Information Engineering College East University of Heilongjiang Harbin China Intelligent Manufacturing Engineering College Harbin Huade University Harbin China

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

In the traditional FBD algorithm based on traditional phase-locked loop (PLL), the detection accuracy and anti-interference performance are often limited when the grid voltage contains DC components and harmonic interference. In order to solve this problem, this paper proposes a phase-locked loop using a double second-order generalized integrator (DSOGI) of a complex notch filter (CNF) to improve the performance of the FBD algorithm. In this method, a second-order generalized integrator is added to the traditional phase-locked loop, and a complex notch filter is connected in series in the error processing link to effectively filter out the DC component and high-interference specific harmonics, obtain the grid synchronization information, and improve the ability to obtain the fundamental wave. The simulation results show that the proposed algorithm can quickly and effectively detect the harmonic current of the grid load under complex grid conditions, and significantly improve the accuracy of harmonic current detection.

关键词： Accuracy Simulation Voltage Harmonic analysis Steady-state Synchronization Phase locked loops detection algorithms Power harmonic filters Notch filters

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Road Trash detection Algorithm of RT-DETR Integrated with State Space

Road Trash Detection Algorithm of RT-DETR Integrated with St...

引用

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

作者： Zexing Luo Guoqing Yang Xinyong Zhao Yeqin Shao School of Transportation and Civil Engineering Nantong University Nantong China Suzhou Research Institute of Industrial Technology Zhejiang University Hangzhou China Hualuyiyun Technology Co. Ltd NanJing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

As global urbanization progresses rapidly, the issue of urban road trash is becoming increasingly severe. Traditional manual cleaning methods are inefficient and struggle to meet the growing demand. This paper proposes an improved road trash detection algorithm based on RT-DETR (Real-Time detection Transformer) and State Space Model (SSM) to enhance detection accuracy and robustness. The algorithm incorporates an innovative feature pyramid structure and a state-space-based internal scale feature enhancement module, effectively addressing object detection in complex scenes as well as challenges associated with occlusions and small object detection. We constructed a diverse urban road trash detection dataset comprising over 60,000 images, providing a solid foundation for the training and testing of the algorithm. Experimental results show that the proposed model improves the mean average precision (mAP) on the COCO dataset by 0.8 percentage points and achieves a 1.2 percentage point improvement on our custom road trash detection dataset, with a 2.1 percentage point increase in the precision of small object detection. The proposed algorithm achieves a favorable balance between real-time performance and accuracy, significantly enhancing trash detection in complex environments, particularly in terms of small object recognition and detection accuracy under occlusion conditions.

关键词： Accuracy Roads Computational modeling Urban areas Object detection Feature extraction Robustness Real-time systems Object recognition detection algorithms

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An improved YOLOv8-based safety helmet wearing detection algorithm

An improved YOLOv8-based safety helmet wearing detection alg...

引用

Computer Information Science and Application Technology (CISAT), International Conference on

作者： Shiyong Ning Xu Han Computer and Information Engineering Department Harbin University of Commerce Harbin China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

Whether to wear a safety helmet is one of the key measures to ensure the safety of personnel in the construction process. This paper proposes a new detection algorithm for safety helmet wearing. Utilizing the YOLOv8n architecture, the algorithm incorporates a SENetV2-based channel-wise attention mechanism into the network’s backbone, thereby giving full consideration to global information, thereby allowing the network to allocate more attention to the safety helmet. To overcome the deficiencies of the initial backbone in combining features, we substitute the original network’s neck section with the CCFM module, thereby improving YOLOv8n’s capacity for feature integration following the extraction of features from the backbone network. A new detection head, DyHead3, is proposed to replace the detection head in the original network model. The empirical findings indicate that the algorithm enhances the mAP, P, and R metrics by 2.02%,3.41%,and 1.07%, respectively, when contrasted with the baseline YOLOv8n approach, respectively, which fully demonstrates the effectiveness and practicality of the improved algorithm proposed in this paper.

关键词： Measurement Information science Head Computational modeling Buildings Feature extraction Safety Neck Personnel detection algorithms

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On-Road Water detection and Water Film Height Estimation using a Time-of-Flight Camera

On-Road Water Detection and Water Film Height Estimation usi...

引用

International Conference on Intelligent Transportation

作者： Dzenana Puscul Armin Schönlieb Andreas Ramsauer Cornelia Lex Institute of Automotive Engineering Graz University of Technology Graz Austria Development Center Graz Infineon Technologies AG Austria Department for Powertrain Engineering (PTE) AVL List GmbH Austria

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

In this article, a methodology to detect the presence of water on the road with a time-of-flight (ToF) camera is presented and validated with measurements from a proving ground. The water detection algorithm distinguishes between dry and wet roads and is based on the measurement data of two time-of-flight imagers. Furthermore, the water film height is estimated with a feedforward neural network. The presented measurements were carried out on the stationary vehicle. The variations of measured conditions include different lighting conditions (daylight, artificial light at night, dawn period) as well as dry and wet asphalt with different water film levels. The results show that the water detection is reliable and the water film height can be well estimated by the neural network.

关键词： Asphalt Accuracy Roads Estimation Cameras Artificial light Feedforward neural networks Reliability detection algorithms Intelligent transportation systems

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Hybrid GMM-GS Message Passing detection for OTFS Modulation

Hybrid GMM-GS Message Passing Detection for OTFS Modulation

引用

IEEE Conference on Wireless Communications and Networking

作者： Wenfang Fan Yuehua Ding Yide Wang School of Electronic and Information Engineering South China University of Technology Guangzhou China Institut d'Electronique et Des Technologies du NumeRique CNRS UMR6164 Nantes Université Nantes France

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Orthogonal Time Frequency Space (OTFS) modulation can effectively combat the Doppler effects and exploit the time-frequency resources. The sparsity of the OTFS channel matrix in delay-Doppler domain plays a crucial role in message passing detection algorithms. This paper presents a hybrid detection approach that combines Gaussian mixture model (GMM) with Gaussian approximation (GS). According to the path gain, GMM is employed to deal with significant paths and GS for less significant paths. The proposed hybrid method achieves a balance between computational complexity and performance by indirectly computing messages and introducing a damping factor. Simulation results show that the proposed method exhibits noteworthy Bit Error Rate (BER) performance and reduces the computational complexity associated with GMM.

关键词： Damping Time-frequency analysis Message passing Simulation Bit error rate Modulation detection algorithms

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High-precision 3D Facial Landmark detection with Curvature-fused Graph Attention Network

High-precision 3D Facial Landmark Detection with Curvature-f...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Juncheng Han Yuping Ye Di Wu Shiyang Long Zhan Song Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China School of Smart Marine Science and Engineering Fujian University of Technology Fuzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the rapid advancement of deep learning, 2D facial landmark detection algorithms have achieved satisfying results. However, due to the absence of large-scale accurately annotated 3D facial datasets, most current 3D facial landmark detection algorithms rely on 2D texture assistance or non-real digital 3D faces. The performance of these algorithms is limited by the accuracy of 2D texture mapping onto 3D faces and the difference between digital faces and actual faces. To tackle these challenges, we have built a large-scale, high-precision 3D facial database using a structured light system. Facial landmarks within the database are marked multiple times to ensure accuracy. Building upon this foundation, we proposed a novel point cloud sampling method and 3D facial landmark detection algorithm. This method utilizes a curvature-fused graph attention network (CGAT) to directly predict landmark coordinates from 3D point clouds. Initially, we extracted a subsampled point set carrying curvature information from the original 3D facial point cloud via geometric point sampling (GPS). Then, we used curvature-encoded positional information as the learning component of the attention module and employed it as a feature extractor to construct the CGAT. We assessed the performance of CGAT on two datasets, BU-3DFE and CIE-H3DF (Ours). Compared to the existing facial landmark detection algorithms, CGAT achieves higher accuracy.

关键词： Point cloud compression Three-dimensional displays Accuracy Databases Feature extraction Sampling methods Data mining detection algorithms Faces Global Positioning System

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