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检索条件"主题词=detection algorithms"

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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...

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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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A novel method of detection of Hot spots in Solar Panels using Deep learning-based Object detection Algorithm

A novel method of Detection of Hot spots in Solar Panels usi...

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BIT Conference (BITCON), International

作者： Rakesh Rohan D K Tanti Mukesh Chandra Department of Electrical Engineering BIT Sindri Dhanbad Jharkhand Production and Industrial Engineering BIT Sindri Jharkhand

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

The formation of hot spots in solar panels is a critical aspect of a photovoltaic system. These anomalies can significantly affect the efficiency and longevity of the panels. In this paper, a deep learning-based objection detection algorithm is used to detect the hot spots in solar panels in a more efficient and easy way. The hotspot dataset is collected using a thermal camera from installed solar panels of BIT Sindri, Dhanbad. The thermal images are manually annotated using Roboflow. The mean average precision (mAP) for the different classes of hotspot achieved a satisfactory result. The object detection technique YOLO has proven highly responsive, highlighting its suitability for practical use for preventative maintenance of photovoltaic panels.

关键词： YOLO Cameras Real-time systems Maintenance Solar panels 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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Analyze the Techniques of Community detection in Social Networks and Their Applications

Analyze the Techniques of Community Detection in Social Netw...

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Sonia Choudhary Seema Malik R. K. Yadav Dept. of CSE Raj Kumar Goel Institute of Technology Uttar Pradesh India Dept. of Industrial IOT Vivekananda Institute of Professional Delhi India Dept. of ECE Raj Kumar Goel Institute of Technology Uttar Pradesh India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

The World Wide Web's emergence paved the way for various social networking sites, facilitating easy communication and connection among users on a shared platform. A social network is essentially a graph comprising nodes and edges, representing users and their interactions, respectively. Common interests naturally draw people together, fostering the formation of communities within social networks. This community formation, in turn, leads to community detection, a process applicable in diverse fields such as finance, politics, marketing, education, and the medical field. This paper explores current3 algorithms for detecting communities in social networks and delves into potential algorithms utilizing deep learning for this purpose.

关键词： Deep learning Social networking (online) Image edge detection Semantics Education Finance Evolutionary computation Web sites Data mining detection algorithms

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SM-LightNet: A Lightweight Ship detection Algorithm Based on Improved YOLOv8

SM-LightNet: A Lightweight Ship Detection Algorithm Based on...

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Academic International Symposium on Optoelectronics and Microelectronics Technology (AISOMT)

作者： Zhiguang Gao Xianwei Rong Xiaoyan Yu School of Computer Science and Information Engineering Harbin Normal University Harbin China School of Physics and Electronic Engineering Harbin Normal University Harbin China

ISBN: (数字)9798331542283

ISBN: (纸本)9798331542290

This article presents a streamlined ship detection algorithm SM-LightNet based on YOLOv8 improvement, which is suitable for environments such as ports with dense ship traffic and complex maritime navigation. Firstly, we introduce a Slim-neck structure that includes a lightweight convolution called GSConv and VoVGSCSP structure. It replaces the traditional convolution in the Neck part of YOLOv8 with GSConv, achieving real-time detection of edge devices and effectively reducing the complexity of computation and parameters. Secondly, replacing the C2f structure in Neck with VoVGSCSP enables the model to have a more concise network structure, which can effectively fuse feature information and enhance the precision and stability of object detection. Finally, the loss function MPDIoU was introduced, which calculates IoU by minimizing the point distance between the estimated boundary box and the actual boundary box, simplifying the calculation process and improving detection accuracy. The outcomes of the experiments indicate that the accuracy of the lightweight algorithm reaches 98.2%, and the GFLOPs of the algorithm are 7.4, which is 9.5% lower than the performance of the original model. The model parameter size is 2.80M, which is 7.5 % lower than the original model.

关键词： Accuracy Convolution Computational modeling Object detection Feature extraction Stability analysis Real-time systems Neck Marine vehicles detection algorithms

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Fire detection Based on Improved YOLOv8 Network Mode

Fire Detection Based on Improved YOLOv8 Network Mode

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

作者： Longling Shao Wanjun Yu Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

In the traditional field of fire detection, a significant amount of specialized equipment and highly trained professionals are typically required. However, the operational efficiency of these methods often tends to be low, and they are susceptible to false alarms, making it challenging to effectively meet the urgent demands of routine fire safety inspections. Additionally, current fire detection technologies based on neural network image recognition also face challenges in terms of realtime performance and accuracy. To enhance the performance of fire detection, this study proposes a fire detection algorithm based on improved YOLOv8, termed FD-YOLOv8 (Fire detection- YOLOv8). On a self-built fire dataset, Fire_dataset, the mean Average Precision (mAP) at intersection over union (IoU) thresholds of 0.5 and 0.5 to 0.95 are 53.6% and 30.7%, respectively.

关键词： Measurement Accuracy Convolution Neural networks Interference Inspection detection algorithms Informatics Optimization Fire safety

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MRM-LOOP:A Dynamic Visual Loop Closure detection Algorithm Based on Motion Region Matching

MRM-LOOP:A Dynamic Visual Loop Closure Detection Algorithm B...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Zhiming Zhang Jun Zhang Yuansheng Liu Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China College of Robotics Beijing Union University Beijing China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Aiming at the problem of false negatives and false positives in loop closure detection for visual SLAM algorithms in dynamic environments, a dynamic visual SLAM loop closure detection algorithm based on motion region matching named MRM-LOOP is proposed. Firstly, the VLAD local feature descriptor algorithm is used to extract the feature vectors of image feature points to ensure the stable correspondence between different images; Secondly, it uses the inter-frame motion vectors extracted from IMU data to enhance the robustness of feature matching and avoid false positive detection caused by camera pose mismatching; Finally, a region-weighted matching strategy is introduced to achieve more accurate loop closure detection by comparing the vector matching degree between the current frame and the historical frame and calculating the matching weight. The experimental results show that the proposed method can significantly improve the recall rate of loop detection in the open data set while maintaining the same accuracy, and the true positive detection probability is increased by 27.29% compared with the DBOW bag method when facing camera rotation. Correct loop detection can be realized in real dynamic environment data set. This paper provides a new solution for visual SLAM technology in dynamic environment.

关键词： Visualization Simultaneous localization and mapping Accuracy Heuristic algorithms Dynamics Feature extraction Vectors Robustness Data mining detection algorithms

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