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检索条件"主题词=detection algorithms"

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An Automatic Tooth Position and Dental Disease detection Algorithm Based on YOLOv8

An Automatic Tooth Position and Dental Disease Detection Alg...

引用

International Conference on Computer and Communications (ICCC)

作者： Hongliang Wang Ke Diao Lin Wu Xuerao Li Xin Zhou Xiaozhou Liu Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School and Hospital of Stomatology China Medical University Liaoning Provincial Key Laboratory of Oral Diseases Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

With the development of digital dental imaging technology, panoramic tomographic images have become an effective tool for diagnosing dental diseases. However, due to the diversity and complexity of dental lesions, accurately identifying tooth positions and pathological conditions from these images remains challenging. To address the issue of low target detection accuracy caused by the high similarity and complex textures of teeth, this paper introduces YOLOv8-Teeth, an enhanced teeth detection algorithm designed for accurate identification of teeth positions and dental diseases from panoramic radiographs. Key innovations include integrating the C2f_ConvNeXtv2 module for improved feature extraction, employing the ASPP module for optimized multi-scale feature capture, and utilizing Focal-EloU Loss for refined bounding box regression. Experimental results on a custom dataset of 476 panoramic radiographs demonstrate significant performance gains, achieving a mean average precision (mAP) of 91.3%, precision of 93.6%, and recall of 88.7%. These improvements offer promising potential for early diagnosis and treatment planning in dental care.

关键词： Technological innovation Accuracy Computational modeling Teeth Tomography Feature extraction Real-time systems Dentistry detection algorithms Diseases

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IVDETR: A Multi-Source Image Pig detection Method Based on Cross-Domain Feature Fusion

IVDETR: A Multi-Source Image Pig Detection Method Based on C...

引用

Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Rui Song Hao Li Mengzhen Huang Zihan Huang Peng Li Xuwen He School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

In large-scale centralised pig farming, object detection algorithms can accurately track and monitor each pig. However, a significant proportion of current detection methods are based on visible images, and the colour differences between different pig breeds limit the applicability of the models. Furthermore, complex background interference, low illumination conditions, or high illumination intensity noise in visible scenes can likewise lead to reduced accuracy during detection. Therefore, we propose a cross-domain encoder based on the self-attention and design the IVDETR model, which integrates features from infrared and visible light images for pig object detection. In order to facilitate the training and evaluation of the model, we constructed a comprehensive, standardised infrared-visible light dataset for the first time. The experimental outcomes demonstrated that the detection accuracy was increased from 0.939 to 0.998 in a real pig breeding environment.

关键词： Training Accuracy Noise Lighting Object detection Feature extraction Pattern recognition detection algorithms Monitoring Farming

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detection and Classification of AI-Generated Text

Detection and Classification of AI-Generated Text

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Shreya Gupta Deepa Gupta Department of Mathematics Indian Institute of Technology Delhi Delhi India Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The advent of Large Language Models (LLMs), has largely transformed global interactions with artificial intelligence. These models, though useful in tasks ranging from translation to text generation, have now permeated everyday life, thereby raising concerns about their potential misuse, particularly in educational institutions. Despite measures like access restrictions implemented by educational institutions, AI based plagiarism has continued to grow, with existing plagiarism detection tools exhibiting limited efficacy in curbing the problem. This paper addresses the need for better AI detection algorithms, focusing on the detection of subtler forms of plagiarism facilitated by LLMs. Leveraging insights from existing studies, the research explores the efficacy of various boosting based machine learning models and dimensionality reduction techniques in improving the accuracy of detecting AI-augmented essays.

关键词： Dimensionality reduction Accuracy Plagiarism Large language models Computational modeling Focusing Boosting Distance measurement detection algorithms

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DynaDetect2.0: Improving detection Accuracy of Data Poisoning Attacks

DynaDetect2.0: Improving Detection Accuracy of Data Poisonin...

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Cyber Awareness and Research Symposium (CARS)

作者： Sabrina Perry Yili Jiang Fangtian Zhong Jiaqi Huang Sohan Gyawali Department of Computer and Information Science The University of Mississippi USA Department of Computer Science Georgia State University USA Gianforte School of Computing Montana State University USA School of Computer Science and Cybersecurity University of Central Missouri USA Department of Technology Systems East Carolina University USA

ISBN: (数字)9798350386417

ISBN: (纸本)9798350386424

Machine learning (ML) models have become pivotal in various sectors, making their security and reliability increasingly important. Data poisoning attacks corrupt training data and pose significant threats to model integrity and performance. Traditional K-Nearest Neighbor (KNN) is vulnerable to these attacks due to its fixed selection of the k value, which limits its ability to adapt to poisoned data. To address this challenge, DynaDetect [1], a dynamic KNN-based detection algorithm, was designed by adjusting k values dynamically. To further improve the detection accuracy of DynaDetect, in this work, we propose DynaDetect2.0, an improved version that builds on the original DynaDetect by incorporating Convolutional Neural Networks (CNN) for feature extraction and utilizing advanced distance metrics, including Mean Squared Error (MSE) and Mahalanobis distance. These improvements enable DynaDetect2.0 to better adapt to attack patterns and significantly increase detection accuracy. Through extensive experimentation, its results indicate that DynaDetect2.0 outperforms both DynaDetect and traditional KNN on diverse datasets such as CIFAR-10, ImageNet, and GTSRB. Specifically, DynaDetect2.0 shows a 34.3% improvement over DynaDetect algorithm and a 106.7% improvement over traditional KNN. This research provides a robust framework for detecting data poisoning attacks in ML models, addressing critical gaps in current detection methods.

关键词： Measurement Accuracy Heuristic algorithms Nearest neighbor methods Feature extraction Real-time systems Classification algorithms Convolutional neural networks detection algorithms Image classification

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Deep Fake detection Based on Hybrid Approach Involving Residual Network Deep Learning Architecture

Deep Fake Detection Based on Hybrid Approach Involving Resid...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： N.V. Shibu R. Muthunagai S. Prabu Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu India Department of Electrical & Electronics Engineering Manakula Vinayagar Institute of Technology Puducherry India Department of Computing Technologies School of computing SRM Institute of Science and Technologies Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

This Deep fake Technology aims at generating synthetic video content or morphed video content which is very realistic to the naked eye. It involves the use of deep learning algorithms in the domain of video processing. In most cases, such artificially generated video contents are very difficult to get differentiated from naturally captured video signals. The existing methodologies of detection involved image tamper detection and digital forensics algorithms. These algorithms are ineffective in detecting videos tampered using deep fake technology. The proposed technique involves training and building of deep learning models that works like a black box to detect the artefacts crucial in the detection of video tampering. This paper presents an overview of the technology advancements in the field of deep fake video detection techniques over recent years. This paper also performs a comparative analysis on the performance of hybrid algorithms involving residual network deep learning architecture in the detection of video signals morphed using deep fake technology. The training datasets are obtained from Face forensics to build the model. The algorithm is augmented with image quality assessment, temporal consistency analysis and facial landmarks analysis to improve the detection algorithm. The performance metrics involve the generation of confusion matrix and generation of accuracy measures. The results provide a promising direction towards the construction of better detection models to safeguard against current and future threats.

关键词： Training Deep learning Measurement Image quality Deepfakes Current measurement Digital forensics detection algorithms Faces Residual neural networks

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Overhead Power Line detection Based on Curriculum Learning Strategy

Overhead Power Line Detection Based on Curriculum Learning S...

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Data Science and Information Technology (DSIT), International Conference on

作者： Zhang Gefei Chen Quan Liu Bingheng Ma Hao Zhang Yihan Cui Zanchen Yang Fan Tong Ying School of Communication and Artificial Intelligence School of Integrated Circuits Nanjing Institute of Technology Nanjing China School of Electrical Power Engineering School of Shen Guorong Nanjing Institute of Technology Nanjing China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

Drone inspection of overhead power lines is a crucial aspect of ensuring electrical safety. Due to the elongated shape of overhead power lines and the complex inspection backgrounds, commonly used detection networks struggle to learn the unique features of these targets. To achieve high-precision detection of overhead power lines, a detection method based on a curriculum learning strategy is proposed. First, multiple target bounding boxes are annotated for individual overhead power lines in aerial images. Next, simulating the human learning process of starting with easier tasks and progressing to more difficult ones, the wavelet-entropy algorithm is used to divide the dataset into simple and complex subsets, allowing the YOLOv5 object detection network to first learn from the simple data and then from the complex data. Finally, a false detection box removal algorithm based on directional gradient statistics is introduced to process the overhead power line images that still contain incorrect identifications after network detection, eliminating the erroneous detection boxes. Experimental results indicate that, under the same network scale, high-precision detection of overhead power lines has been achieved, with an accuracy improvement of 10.8% compared to YOLOv5 based on traditional training strategies, and mAP_0.5 reaching 78.6%. This method effectively detects slender targets like overhead power lines while minimizing background interference, advancing the development of drone-based electrical inspection technology.

关键词： YOLO Training Point cloud compression Accuracy Shape Inspection Real-time systems Reliability detection algorithms Drones

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Covariance-Based Activity detection Algorithm in Clustered Cell-Free Massive MIMO

Covariance-Based Activity Detection Algorithm in Clustered C...

引用

Computer and Information Processing Technology (ISCIPT), International Symposium on

作者： Haowen Ye Ranran Lv Kai Sun Wei Huang School of Electronic Information Engineering Inner Mongolia University Hohhot China

ISBN: (数字)9798350388404

ISBN: (纸本)9798350388411

Massive Machine-Type Communications (mMTC) plays an increasingly important role in future wireless communications, and the use of a large number of low-power devices in conjunction with cell-free massive MIMO networks is a promising enabling technology. In this research, we explore activity detection for Grant-Free Random Access in massive mMTC within a cell-free Massive MIMO network employing distributed antenna arrays. Each active device sends a non-orthogonal pilot sequence to geographically clustered Access Points (APs) using the K-means algorithm. The clustered APs then relay the received signals to the Central Processing Unit (CPU) for collaborative activity detection. Simulation results demonstrate that for extensive coverage areas, the covariance-based activity detection algorithm with K-means clustered APs exhibits reduced complexity and improved activity detection efficiency while maintaining robust performance.

关键词： Wireless communication Simulation Partial transmit sequences Clustering algorithms Massive MIMO Central Processing Unit detection algorithms

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Insulator detection Based on Feature-Enhanced Convolution Attention Method

Insulator Detection Based on Feature-Enhanced Convolution At...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Hangfan Dong Jun Feng Yijie Wang Yang Li Yibo Yu Binyu Xiong School of Automation Wuhan University of Technology Wuhan China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

With the purpose of solving the problems of low inspection efficiency and high field inspection danger encountered in current insulator detection, in this paper we propose the Feature-Enhanced Convolution Attention YOLO (F-ECA YOLOv8), an improved YOLOv8 insulator detection algorithm for infrared images. Firstly, the ordinary convolution in C2 is substituted by the Depthwise Separable convolution, improving the target detection accuracy which enhance its adaptive ability and reducing the computational complexity. Next, the Fusion-SE-Coord Attention(F-SCA) module is designed to improve the model's ability to adapt to key features and to capture channel features and spatial features, which is able to fuse global and local information effectively. Finally, WIOU is used to replace the original loss function to further address the issue of the unbalance quality of the sample. Compared to the YOLOv8s,the F-ECA algorithm scored 0.5% and 6.7% higher in mAP@0.5 and mAP@0.5-0.95 separably, indicating that the algorithm can be effectively applied to detect insulators in infrared images in practical scenes.

关键词： YOLO Accuracy Convolution Fuses Energy Internet System integration Inspection Insulators Feature extraction detection algorithms

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Research on a SAR Image Ship detection Algorithm Based on Feature Refinement

Research on a SAR Image Ship Detection Algorithm Based on Fe...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Zhou Yucheng Gong Yan Yao Zhixuan China Waterborne Transport Research Institute Beijing China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

SAR images have long been plagued by coherent speckle noise and challenges in detecting small ships within the field of ship detection. This paper introduces a synthetic aperture radar (SAR) image ship detection algorithm based on feature refinement. The algorithm employs a denoising module to mitigate noise interference and captures more spatial position information through a hierarchical feature fusion (HFF) module, enhancing the detection precision of small ships. Lastly, the algorithm leverages a feature refinement (FR) module based on deformable convolution to minimize clutter signal interference, thus enhancing the accuracy of ship positioning. Experimental results demonstrate that the algorithm exhibits high accuracy and robustness in detecting ships from SAR images.

关键词： Accuracy Convolution Noise Noise reduction Speckle Feature extraction Radar polarimetry Robustness Marine vehicles detection algorithms

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DriSafePh: An IoT Based Realtime Driver Drowsiness detection System using Hybrid Machine learning Algorithm

DriSafePh: An IoT Based Realtime Driver Drowsiness Detection...

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International Conference on Software Technology and Engineering (ICSTE)

作者： Kenneth Von C. Golosinda Jasper Van D. Marcellones Luke Jellergil P. Ampongol Neil P. Magloyuan Patrick D. Cerna Mapua Malayan Colleges Mindanao Davao City Philippines

ISBN: (数字)9798350378955

ISBN: (纸本)9798350378962

Driver drowsiness contributes significantly to road accidents worldwide, and while drowsiness detection systems have already been implemented on higher-end cars, DriSafePh introduces an embedded system using a Raspberry Pi that can be implemented in any enclosed vehicle. DriSafePh aims to address the prevalent issue of driver drowsiness on the road by developing a real-time driver drowsiness detection system and leveraging a hybrid machine learning algorithm that processes facial landmarks and gestures to accurately detect drowsiness, which has yet to be used in its field and will be compared to other algorithms already in use. Additionally, the researcher aims to provide a warning notification to the driver through a text-to-speech algorithm when the appropriate drowsiness level is detected and optimize the machine learning model's performance. The study conducted a survey to gather drivers' opinions on a drowsiness detection system and their personal experiences with drowsy driving. The results showed that the respondents viewed the working prototype positively. Additionally, the data revealed that more than 70% of the respondents had experienced drowsy driving, highlighting the need for such a device. The recommended approach of the DriSafePh model was evaluated using a performance matrix, and the detection for the face, eye, and mouth provides a high accuracy level of 96-99%. In the meantime, eye closure and yawn detection accuracy were able to match the other existing algorithms on the Raspberry Pi device with an accuracy of 96.89% and 85.93%, respectively.

关键词： Performance evaluation Surveys Machine learning algorithms Accuracy Software algorithms Machine learning Software Text to speech detection algorithms Vehicles

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