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检索条件"主题词=Detection Algorithms"

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detection of Pantograph Angle Deficiency Based on Optimized YOLOv8n Model

Detection of Pantograph Angle Deficiency Based on Optimized ...

引用

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

作者： Yifeng Wang Hu Sheng Na Geng Wenqiang Li School of Computer and Communication Engineering Dalian Jiaotong University Dalian China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

A optimized YOLOv8 detection algorithm, UIB-YOLOv8, is proposed to address the problems of high missed and false detection rates in pantograph fault detection, as well as difficulty in identifying pantograph angles in environments with insufficient lighting. A key point detection algorithm based on a high-resolution feature pyramid network is used to extract the position of the pantograph angle. The UIB module is introduced into the backbone network to enhance feature extraction in the image. The MQA attention mechanism is combined to reduce the number of model parameters and improve detection speed. Finally, a Focal Loss loss function is added before the result output to reduce the problem of imbalanced sample categories. The experimental results show that UIB-YOLOv8 improves MAP by 2.2%, reduces parameter count by 26.35%, and reduces floating-point computation by 24.36% compared to YOLOv8 algorithm. Especially in low light conditions, the accuracy of identifying pantograph angle is improved by 4.8%.

关键词： Fault diagnosis Deep learning Computer vision Machine learning algorithms Computational modeling Fault detection Lighting Object detection Feature extraction detection algorithms

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Application of HHT Algorithm to ECG QRS detection

Application of HHT Algorithm to ECG QRS Detection

引用

International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM)

作者： Bochen Chen Dan Ye Bo Jiang Lifeng Lin Bingli Jiao School of Electronics Peking University Beijing China General Administration of Customs Beijing China Guangzhou Nursing Home Guangzhou China

ISBN: (数字)9798350368802

ISBN: (纸本)9798350368819

Electrocardiogram (ECG) signals are the most important tool in the diagnosis of cardiovascular diseases, and the QRS complexes are the most significant waveforms within ECG signals, representing the critical steps of the heart's pumping function. This study integrates the Hilbert-Huang Transform (HHT) into conventional ECG detection algorithms, which improves the real-time detection method of QRS complexes based on traditional differential thresh-old techniques. The effectiveness of the proposed method is validated using simulation results from the MIT-BIH ECG dataset provided by the Massachusetts Institute of Technology. The results indicate that the QRS complexes detection rate of the HHT-based ECG monitoring algorithm is significantly higher than that of traditional methods, thereby confirming the superiority of this approach.

关键词： Heart Filtering Simulation Noise reduction Transforms Electrocardiography Real-time systems detection algorithms Monitoring Testing

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A Distributed Critical Node detection Algorithm for UAV Swarm Networks

A Distributed Critical Node Detection Algorithm for UAV Swar...

引用

Computing, Communication, Perception and Quantum Technology (CCPQT), International Conference on

作者： Zihang He Haosong Gou Xiong Wu Gaoyi Zhang Pengfei Du Daosen Zhai School of Electronics and Information Northwestern Polytechnical University Xi'an China Sichuan Co. Ltd. China Mobile Group Chengdu China School of Physics and Electronics Hunan University Changsha China Engineering Research Center of Intelligent Airground Integrated Vehicle and Traffic Control Ministry of Education Xihua University Chengdu China

ISBN: (数字)9798331528386

ISBN: (纸本)9798331528393

Unmanned aerial vehicle (UAV) swarm can be widely used in cooperative detection and strike across wide geographic areas. To support the collaboration of multiple UAVs, a connected network topology is essential. Thus, critical nodes, whose removal will disconnect the network into two or more separate components, will play an important role in UAV swarm networks. This paper investigate the distributed critical node detection algorithm for UAV swarm networks. Firstly, we analyze the logical relationship between critical node, robustness, and k-hop subgraph. Guided by the analysis, we propose a distributed critical node detection algorithm by using the geometrical relationship of neighbors and the k-hop local topology. Abundant simulation results indicate that the proposed algorithms can strike a good balance between accuracy and overhead and greatly outperform the global detection algorithms especially in large-scale networks. Besides, the simulation study provides some useful design guidance for robust topology control of UAV swarm networks.

关键词： Accuracy Quantum computing Network topology Simulation Quantum mechanics Collaboration Autonomous aerial vehicles Robustness Topology detection algorithms

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Video Matting Tamper detection Based on Compressed Tracking

Video Matting Tamper Detection Based on Compressed Tracking

引用

Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Zhiwen Chen Xiling Yang Computer Science Wuhan Railway Vocational College Of Technology Wuhan China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

In the digital era, the authenticity of video content has become a significant concern, especially given the rapid advancements in video technology and the increasing potential for tampering. This poses risks to information reliability and impacts areas like legal proceedings, media integrity, and personal privacy. This paper introduces a novel video matting tamper detection method that employs compressed tracking to ascertain the authenticity of video subjects. The method involves several precise steps: initially, edge detection highlights potential tampering by outlining object boundaries. Feature analysis then examines elements like color and texture for anomalies, and target tracking monitors object movements over time to identify inconsistencies. A final comparison of these results with known authentic characteristics helps pinpoint tampering signs. Extensive testing has proven the method’s effectiveness in detecting various tampering types accurately, offering a reliable tool for verifying video authenticity and contributing valuable insights to the field of information security.

关键词： Technological innovation Privacy Target tracking Image edge detection Media Reliability Object recognition detection algorithms Monitoring Testing

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Crack detection Algorithm Based on Improved YOLOv11n Network

Crack Detection Algorithm Based on Improved YOLOv11n Network

引用

Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE International Conference on

作者： Yuxin He Ailing Ding School of Information Engineering Changan University Xi'an China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

The detection algorithms for various engineering structure cracks often suffer from missed detections, false positives, and low accuracy. This paper proposes an improved crack detection model based on Yolov11, integrating RFCBAM and Triplet Attention mechanisms. Experimental results show that this model performs better in detection compared to the original model and those improved by a single mechanism. The metrics for Precision (P), Recall (R), mAP 50 , and mAP 50−95 all reach high levels.

关键词： YOLO Computer science Attention mechanisms Accuracy Sensitivity Target recognition Data mining detection algorithms Monitoring Intelligent control

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Enhancing Deepfake detection with a Hybrid CNN-BiLSTM Approach

Enhancing Deepfake Detection with a Hybrid CNN-BiLSTM Approa...

引用

International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Harsh Kumar Verma Isha Kumar Department of Computer Science and Engineering Dr. B.R Ambedkar National Institute of Technology Jalandhar India

ISBN: (数字)9798331518127

ISBN: (纸本)9798331518134

Rapid improvements in deepfake technology have created serious threats to the integrity of digital information. Deepfakes have the potential to be employed for intimidating or blackmailing specific public figures, as well as for deceiving the general public. Concurrently, the advancement in deepfake technology has led to notable progress in developing detection algorithms. Primarily, these algorithms concentrate on analyzing images rather than delving into the temporal progression within video content. In the information era of today, detecting deepfakes has become more important than ever. The paper posits a pioneering hybrid approach for detecting deepfake videos, combining the scrutiny of human eye blinking patterns, utilizing Xception image characteristics, and implementing a Bidirectional Long Short Term Memory network (Bi-LSTM). This methodology leverages Convolutional Neural Networks(CNN) to extract image attributes and Bi-LSTM networks to scrutinize sequences consecutively. Moreover, it utilizes an adaptive spatio-temporal attention mechanism to concentrate on pertinent frames in the video sequences. The dataset used for experimental purposes includes both authentic and artificially generated videos from the Celeb-DF dataset. The proposed framework attained an accuracy rate of 86% in classification with an area under curve of 0.90, showcasing its efficacy in the identification of deepfake videos.

关键词： Deepfakes Attention mechanisms Accuracy Video sequences Bidirectional long short term memory Convolutional neural networks detection algorithms Machine intelligence Digital information

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Safety Helmet detection Algorithm Based on Improved YOLOv7

Safety Helmet Detection Algorithm Based on Improved YOLOv7

引用

IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Hongtao Deng Min Wang Xun Zhu Liping Zou Can Liu Zhuwei Lin College of Artificial Intelligence Jianghan University WuHan China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

The wearing of helmets is crucial for construction sites, which can protect workers from injuries caused by head impacts and reduce the occurrence of safety accidents. The image size captured by the construction site camera is small, and it is inevitable that there are omissions and wrong detections; considering the complexity of the construction site, there are also the problems of slow detection speed and insufficient detection accuracy, so this paper proposes a helmet detection algorithm based on the improved YOLOv7. The simam module is added to the backbone network of YOLOv7, which makes the model pay more attention to the extraction of main features and reduces the problems of omission and misdetection; based on the NWD method, the loss function is improved to improve the regression accuracy and accelerate the convergence. The experimental results show that the improved algorithm has better results than the original YOLOv7 model by improving the accuracy by 2.1% and mAP by 1.4%.

关键词： Accuracy Head Feature extraction Cameras Safety Complexity theory detection algorithms Information technology Injuries Convergence

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Optimizing Onset and Offset detection in Surface Diaphragm Electromyographic Signals: A Signal Quality-Driven Approach

Optimizing Onset and Offset Detection in Surface Diaphragm E...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Abel Torres Luis Estrada-Petrocelli Tim Raveling Marieke L Duiverman Biomedical Research Networking Centre in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN) Universitat Politècnica de Catalunya-BarcelonaTech (UPC) Institut de Bioenginyeria de Catalunya (IBEC) Barcelona Institute of Science and Technology (BIST) Barcelona Spain Tecnología e Innovación (SENACYT) – Sistema Nacional de Investigación (SNI) Facultad de Ingeniería Universidad Latina de Panamá Secretaría Nacional de Ciencia Panama City Republic of Panama Department of Pulmonary Diseases and Home Mechanical Ventilation University of Groningen University Medical Center Groningen Groningen The Netherlands

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This study explores the detection of onset and offset of diaphragm electromyographic activity in patients with chronic obstructive pulmonary disease (COPD) undergoing nocturnal non-invasive mechanical ventilation (NIV), aiming to enhance monitoring and management strategies for this patient population. The automatic analysis of electromyographic activity allows for the examination of respiratory patterns, addressing the challenges of cumbersome and time-consuming assessments. A signal quality-driven approach is used to improve the automatic detection algorithm for both onset and offset. The results show an improvement in the accuracy of onset and offset detection, with average onset detections advancing the scorer's mean by 194 ms, and offset detections lagging by 285 ms, which were reduced to 29 ms and 119 ms, respectively, after applying the correction algorithm. This approach has potential to enhance the monitoring and management of COPD patients undergoing nocturnal NIV.

关键词： Electric potential Accuracy Lungs Ventilation Chronic obstructive pulmonary disease Engineering in medicine and biology detection algorithms Monitoring

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Road Crack detection Algorithm Based on Improved YOLOV5 Model

Road Crack Detection Algorithm Based on Improved YOLOV5 Mode...

引用

Sensors, Electronics and Computer Engineering (ICSECE), IEEE International Conference on

作者： Guangda Liang Wenwen Liu Weiwei Li College of Environment and Architectural Arts Tianjin Academy of Fine Arts Tianjin China Beijing Institute of Control and Electronic Technology Beijing China School of Computer Science and Technology Tiangong University Tianjin China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

A lightweight road crack target detection method based on YOLOv5 and GhostNet is proposed to address the problems of complex models and high computational complexity in existing road crack detection methods. The GhostConv and GhostC3 modules of the lightweight network GhostNet are introduced to improve the backbone network of YOLOv5, achieving the goal of significantly reducing model computation while ensuring the network's feature extraction capability. The experimental results show that the improved algorithm proposed in this paper significantly reduces the volume of the model and the number of parameters, and at the same time ensures the high precision of detection. Therefore, the model can be used as an ideal lightweight detection model for road crack images.

关键词： YOLO Training Solid modeling Accuracy Computational modeling Roads Feature extraction Sensors Computational complexity detection algorithms

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Power Engineering Safety Helmet detection Algorithm Based on BR-YOLOv7

Power Engineering Safety Helmet Detection Algorithm Based on...

引用

International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Deng Jiatao Yu Junyu Jing Hao Shen Yong Tan Miaoyan Information Technology Division Sichuan Keruide Power Communication Technology Co. Ltd. Chengdu China Guilin University of Information Technology Guilin China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

In the construction of power engineering, wearing a safety helmet is of vital importance. However, due to the problems of dense multi-scale and mutual occlusion of the detection targets during the safety helmet detection process, there are situations such as false detection and missed detection in the detection process. In response to this situation, this paper proposes the BR-YOLOv7 object detection algorithm. First, to enhance the network's feature extraction ability for safety helmets, this paper introduces the BiFormer attention module. This module combines the feature extraction mechanisms of Transformer and BiFPN, enabling the network to better capture the feature information of safety helmets, improving the network's ability to extract the features of safety helmets, thereby enhancing the accuracy and stability of the detection model in complex environments. For the mutually occluded safety helmet detection targets, this paper introduces the repulsion loss function and optimizes it to reduce the loss during the bounding box regression process. Experimental results show that the safety helmet detection accuracy of this algorithm reaches 97.7%, which is 1.9% higher than the original algorithm and meets the requirements of power engineering for the detection accuracy of safety helmets.

关键词： Power engineering Head Accuracy Object detection Feature extraction Transformers Stability analysis Safety detection algorithms Information systems

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