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检索条件"主题词=detection algorithms"

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Fatigue detection Scheme in Complex Environments Based on rPPG and Dynamic Convolution

Fatigue Detection Scheme in Complex Environments Based on rP...

引用

Artificial Intelligence and Electromechanical Automation (AIEA), International Conference on

作者： Haoming Deng Yubin Chen Qingqing Wu Yue Pan School of Software Nanchang Hangkong University Nanchang China

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

Workers who succumb to fatigue not only experience a decline in productivity but are also at an increased risk of accidents. In order to more effectively detect fatigue states within intricate work settings, a novel fatigue detection approach grounded in rPPG technology has been introduced. This approach involves the extraction of rPPG signals from facial videos, followed by the reconstruction of the raw signals through empirical mode decomposition, and subsequently their conversion into two-dimensional time-frequency signals via wavelet transform. Subsequently, a TVCNN-att model is constructed by integrating dynamic convolution techniques and attention mechanisms. The experimental outcomes indicate that the proposed method has yielded satisfactory performance across a range of evaluative metrics.

关键词： Productivity Measurement Time-frequency analysis Continuous wavelet transforms Empirical mode decomposition Convolution Heuristic algorithms Fatigue detection algorithms Videos

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Hybrid Dual-Channel Input Image Tampering detection for Scientific Papers

Hybrid Dual-Channel Input Image Tampering Detection for Scie...

引用

International Conference on Behavior, Economic and Social Computing (BESC)

作者： Jiayi Shi Haihong E Jianhua Liu Tianyi Hu Xiaodong Qiao Junpeng Ding Jiayu Huang School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Beijing Wanfang Data Co. Ltd. Beijing China

ISBN: (数字)9798331531904

ISBN: (纸本)9798331531911

With the development of science and technology, the application of digital image processing and artificial intelligence technology is becoming more and more widespread, which makes it easier and more difficult to detect forged academic images, and academic images have become a high incidence of scientific and technological journal paper forgery. To ensure the authenticity of academic images and scientific reproducibility of experiments, we developed a network called MultiScopeNet. It combines RGB and DCT streams, performs multi-resolution fusion at each layer, and can comprehensively analyze spatial and frequency domain features of images. We also created a new dataset based on an existing tamper detection dataset by applying an image complementation model to train and validate our model against academic misconduct of tampering using generatively forged images. MultiScopeNet significantly outperforms existing state-of-the-art models in dealing with image tampering at different resolutions and sizes.

关键词： Training Visualization Frequency-domain analysis Transform coding Transforms Reproducibility of results Discrete cosine transforms detection algorithms Spatial resolution Streams

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Enhancing Traffic Light detection and Classification through Federated Learning

Enhancing Traffic Light Detection and Classification through...

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IEEE Conference on Vehicular Technology (VTC)

作者： Ioannis V. Vondikakis Ilias E. Panagiotopoulos George J. Dimitrakopoulos Dept. of Informatics and Telematics Harokopio University of Athens (HUA) Athens Greece

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Traffic light detection stands as a pivotal challenge in the realm of connected and automated driving systems and intelligent traffic management. Traffic light detection lies in the real-time and accurate recognition of traffic lights and their states ’go’, ’warning’, ’stop’, ’goLeft’, ’warningLeft’, ’stopLeft’, even under diverse environmental conditions. This paper introduces a synthetic approach for intelligent traffic light detection and classification, namely FL-TLDC, that synergizes federated learning with Fast Region-based Convolutional Neural Networks (Faster R-CNN). The proposed collaborative and decentralised approach allows the collective improvement of traffic light detection algorithms without the need to share sensitive or proprietary data. Simulation results demonstrate that the proposed analysis achieves a high detection performance and provides fast responses.

关键词： Vehicular and wireless technologies Accuracy Federated learning Simulation Collaboration Real-time systems Convolutional neural networks detection algorithms

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Strawberry Maturity detection Algorithm Based on Improved YOLOv9

Strawberry Maturity Detection Algorithm Based on Improved YO...

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Advances in Electrical Engineering and Computer Applications( AEECA), 2020 IEEE International Conference on

作者： Rong Ye Guoqi Shao Hongrui Zhang Tong Li Quan Gao College of Food Science and Technology Yunnan Agricultural University Kunming China The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province Yunnan Agricultural University Kunming China College of Landscape and Horticulture Yunnan Agricultural University Kunming China College of Big Data Yunnan Agricultural University Kunming China

ISBN: (纸本)9798350355260

Deep learning is a crucial research focus within the realm of artificial intelligence, with its utilization in agriculture becoming more prevalent. Strawberries are one of the most popular fruits, but immature strawberries are difficult to attract consumers. This paper aims to enhance the accuracy of strawberry ripeness detection during the picking process by introducing a strawberry ripeness detection model called YOLOv9-CS. The model integrates Cloformer into the backbone network to improve the fusion effect of texture features among strawberries of the same maturity. Additionally, the inclusion of SeaFormer Block in the neck network enhances the model's ability to perceive target positions, thereby boosting detection accuracy. By utilizing the Focal-EIoU loss function instead of the traditional IoU loss function, the model's robustness and accuracy are further improved. Experimental results on a strawberry dataset demonstrate that the YOLOv9-CS model outperforms the original YOLOv9 model, achieving a 2.47% increase in precision, 4.54% in recall, and 4.16% in mAP, reaching 95.72%, 95.59%, and 96.61% respectively, while maintaining real-time detection capabilities. The proposed model significantly enhances the identification of strawberries at different maturity levels in natural settings, catering to the intelligent and automated requirements of smart agriculture for strawberry picking.

关键词： YOLO Smart agriculture Electrical engineering Accuracy Color Robustness Real-time systems Neck detection algorithms Monitoring

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Lightweight detection Algorithm for Corn Insect Pests Based on Attention Mechanism

Lightweight Detection Algorithm for Corn Insect Pests Based ...

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Internet of Things, Automation and Artificial Intelligence (IoTAAI), International Conference on

作者： Haiying Song Yiying Yan Jian Cen Min Xie Zhifeng Li Changhao Guo School of Automation Guangdong Polytechnic Normal University China School of Electricity South China University of Technology China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

The timely and accurate detection of corn pests is of great significance for the prevention and control of corn pest damage. To improve the real-time and precision of corn pest detection, this paper proposes a lightweight YOLOv8s-FS model for corn pest detection. Firstly, the paper introduces an efficient FasterNet network structure into the network architecture, achieving the lightweight of the model. Secondly, a Simple Parameter-Free Attention Module (SimAm) is introduced to build an efficient neck network, which improves the recognition accuracy of the model. Finally, in terms of corn pest detection, compared with the YOLOv8s model, the YOLOv8s-FS model reduces the number of parameters by 25%. The recognition accuracy mAP0.5 is increased by 2%, reaching 89.4%. The YOLOv8s-FS model has the characteristics of being lightweight and high precision, and can accurately identify corn pests.

关键词： Accuracy Attention mechanisms Prevention and mitigation Insects Production Network architecture Real-time systems Neck Internet of Things detection algorithms

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Research on Intelligent detection Algorithm of Small Foreign Objects in Images Based on Deep Learning

Research on Intelligent Detection Algorithm of Small Foreign...

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

作者： Feng Jiliang Ye Wangping Guo Cheng Wang Qiong Lou Hongna Xi’an XK Electric Power Equipment Intelligent Service Co. Ltd Xi’an China Jiangxi Communication Terminal Industry Technology Research Institute Co. Ltd Ji’an China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

To address the challenge of identifying small foreign objects in images, this study introduces an advanced algorithm integrating deep learning with multi-scale detection methodologies. The algorithm constructs a sophisticated multilayer convolutional neural network (CNN) architecture, which thoroughly explores the multi-scale attributes of the image. This is then combined with an anomaly detection framework to achieve precise identification of small foreign objects within images. The model leverages the robust feature extraction capabilities of the CNN to capture fine details across various scales, enabling the effective detection and localization of anomalous regions within the image. To evaluate the algorithm's effectiveness and applicability, a series of simulations were conducted across multiple scenarios. The results from these simulations indicate that the algorithm consistently achieves a detection accuracy rate exceeding 95% in diverse environments, while maintaining a false detection rate below 5%, thereby demonstrating the algorithm's stability and efficiency in handling complex backgrounds. Furthermore, the simulation outcomes reveal that the system exhibits strong real-time performance and resilience when applied to intricate industrial settings, further underscoring the algorithm's significant potential for widespread use in industrial inspection and quality control processes.

关键词： Deep learning Accuracy Computational modeling Quality control Feature extraction Real-time systems Data models Convolutional neural networks Object recognition detection algorithms

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Safety Helmet detection Using Improved YOLOv8

Safety Helmet Detection Using Improved YOLOv8

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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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An Efficient Flow Rule Conflict Comprehensive detection Scheme for SDN Networks

An Efficient Flow Rule Conflict Comprehensive Detection Sche...

引用

International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Bo Han Yuan Liu Yongbin Zhou Yiwen Gao School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Software-Defined Networking (SDN) has introduced flexibility and efficiency to network management but also faces challenges from flow rule conflicts, including static, dynamic, and dependency conflicts. Existing detection algorithms often focus on a single conflict type, resulting in inefficiencies and high false positive rates. To address these issues, we propose a comprehensive flow rule conflict detection scheme that improves real-time detection of explicit conflicts (both static and dynamic) and reduces false positives in implicit (dependency) conflict detection. Specifically, we present a real-time explicit conflict detection algorithm based on the Protocol-Divided Trie (PDT), which categorizes flow rules by protocol type and uses a prefix tree for rapid matching. Experimental results show that this approach significantly reduces detection times by at least 39.2% and achieves 100% detection accuracy. Additionally, we propose a two-stage detection (TSD) algorithm that combines the precision of path-based detection (PBD) with the efficiency of alias set-based detection (ASD). Our experiments reveal a 51% reduction in false positives compared to ASD and a 48% reduction in detection time compared to PBD, while maintaining equivalent false positive rates. This approach provides a robust solution for conflict detection in SDN, improving network security and resource utilization efficiency.

关键词： Distributed processing Protocols Accuracy Network security Real-time systems Resource management Software defined networking detection algorithms Faces

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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...

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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 ...

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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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