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检索条件"任意字段=Proceedings of the 3rd International Conference on Control and Computer Vision"

共 18533 条记录，以下是91-100 订阅

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Detection of Damages in Wire Ropes Using computer vision Techniques 3

Detection of Damages in Wire Ropes Using Computer Vision Tec...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Medaramatla, Sai Charan Samhitha, Chennupati Veda Srinivasa Reddy, K. School Of Computer Science Vellore Institute Of Technology Ap Amaravati India

ISBN: (纸本)9798350318609

Wire ropes are one of the most commonly used objects in industries and at construction sites. These are used to lift heavy objects and are part of cranes. Wire ropes are constantly exposed to all forms of nature such as heat from the sun and the humidity from the rain, additionally, wire ropes lift very heavy weights, which makes them prone to damage. If these damages were gone without notice, they may cause severe accidents. Thus, this study was performed to develop a model that can detect the damages in wire ropes using the deep learning algorithm YOLOv8. YOLOv8 is a computer vision-based system that is precisely known for its best object detection skills. The model was trained using a hybrid dataset consisting of 2892 images. The model showed an accuracy of 98.31%. This developed model using YOLOv8 is reliable for real-time detection of wire rope damage. © 2024 IEEE.

关键词： computer vision

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3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

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3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819616930

The proceedings contain 27 papers. The special focus in this conference is on Machine Learning, Cloud Computing and Intelligent Mining. The topics include: Decentralized Optimal Tracking control of MRMs Under Health Indicator-Based Event-Triggered Mechanism;iterative Learning control for Nonlinear Switched Continuous-Time Systems with Time-Varying Delay;Model Migration and Joint Communication and Computing Resource Allocation in Native AI Wireless Networks;FTB-RRP control for 2-D Continuous-Discrete Systems in Roesser Model;Optimal UAVs Placement for TDOA Localization Method Based On An Improved Grey Wolf Optimization Algorithm;A Bat-BP Optimization Algorithm Based System of Dry Ice Decontamination for UAV;Optimal Resource Allocation Algorithm for Multi-user AI Tasks in Native AI Networks;study on Non-intrusive Load Monitoring Method Based on K-means Clustering and Dual Convolutional Neural Networks;research and Application of computer vision and Predictive Maintenance in Health Management of Conveying Equipment;attitude Stabilization control for Unmanned Aerial Vehicle with Robotic Arm Based on Fixed-Time Observer Active Disturbance Rejection control;state Estimation and control of Switched Neural Networks with Mode-Dependent Hybrid Dwell Time;Distributed Consensus control Research of Unmanned Aerial Vehicle (UAV) Swarms Based on Lennard-Jones Potential;lifetime Prediction of Fuel Cell Composite Operating Conditions Based on Genetic Algorithm Optimized Elman Neural Network;a Tiered Native Digital Twin for Telco Networks;defender: The Possibility of Repairing Jailbreak Defects;research on Special vision Recognition and Fault Alarm System for Vertical Roller;malicious Traffic Classification Algorithm Based on Multimodal Fusion;Enhanced Affine Formation control in Multi-agent Systems with Disturbance Rejection Using RISE controller;model Predictive control for Contour control in Biaxial System.

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Face Recognition using MTCNN, Inception - Resnet with Ensemble Approach 3

Face Recognition using MTCNN, Inception - Resnet with Ensemb...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mahanthesha, U. Tejaswini, R.M. Nagarathna, C.R. Ravikumar, Y. Tejal Bnm Institute of Technology Dept.of Ai and Ml Bangalore India University of Manchester Dept of Advanced Computer Science United Kingdom

ISBN: (纸本)9798331515911

One of the critical task included in the computer vision in face recognition. Security system, surveillance system, computer-human interactions are some of the applications where face recognition has been widely used. This paper proposes an novel face recognition model that inculcates deep learning and feature extraction techniques. Our model, named "Inception-Resnet"is built to efficiently and accurately recognize faces from images. Inception-Resnet employs a deep convolutional neural network and it also employs a deep CNN architecture that includes multiple layers of convolutional and pooling operations for robust feature extraction. The network is trained using a large-scale dataset consisting of labelled face images, that allows it to learn differential facial features. Additionally, Inception-resnet utilizes a loss function during training phase to encourage the network to learn compact and separable face embeddings in the feature space. Face verification is conducted using an ensemble approach which combines multiple classifiers, including Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and logistic regression, for the classification of face embeddings. © 2025 IEEE.

关键词： Convolutional neural networks

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3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

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3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819624676

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2024 3rd international conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

2024 3rd International Conference on Advancement in Electric...

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3rd international conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

ISBN: (纸本)9798350388282

The proceedings contain 180 papers. The topics discussed include: optimizing diabetes prediction accuracy: a comprehensive approach with advanced preprocessing and diverse machine learning classifiers;designing a graphics processing unit for 2D rendering on FPGA for educational purpose;an ensemble approach of transfer learning and vision transformer to identify COVID-19 from chest X-rays;advancing water vending industry through RFID and IoT empowerment;voltage harmonics mitigation of non-linear loads using model predictive control in a three phase system;design and analysis of a 6G terahertz aeronautical antenna based on graphene;circularly polarized single layer large scale microstrip patch array antenna for wireless communications;and enhancing autism spectrum disorder diagnosis through a novel 1D CNN-based deep learning classifier.

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A Comprehensive Analysis of Object Tracking in UAVs using computer vision 3

A Comprehensive Analysis of Object Tracking in UAVs using Co...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Ilavendhan, A. Paul, Bhenedix Pokala, Joshnavi School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331532420

Object tracking has proven to be an essential in a wide range of applications and unmanned aerial vehicles (UAVs) provide advantages to this field in accurately observing and tracking moving targets in challenging environments. This paper presents an overview of various object tracking methodologies which can be utilized on UAVs. It discusses different approaches to attain an improved method of UAVs to help track moving objects accurately, even under challenging conditions such as occlusions or changes in object size and speed. This research study highlights the combinations of traditional tracking techniques infused with modern machine learning techniques to help explore advancements and opportunities of performance improvement. Key topics discussed are the use of algorithms that enable prediction of location of objects, techniques for tracking even small objects in dynamic environments and possibilities of UAVs cooperating with ground vehicles to improve tracking accuracy by sharing data. Overall, the paper discusses the importance of developing more efficient and reliable tracking systems which have applications in various fields such as surveillance, search and rescue operations. © 2024 IEEE.

关键词： Object tracking

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The Deepfake Dilemma: Enhancing Deepfake Detection with vision Transformers 3

The Deepfake Dilemma: Enhancing Deepfake Detection with Visi...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Sumathi, D. Singh, Ashu Sinha, Arpita Aditya, D. Mohammed Riyaan, K.F. Alliance University Department of Computer Science and Engineering Bangalore India Alliance University Department of Information Technology Bangalore India

ISBN: (纸本)9798331515911

The emergence of deepfake videos at an alarming pace has compromised the integrity of digital multimedia and mandates progressive research into detection strategies. A new forensic method for subjecting face tampering detection in videos using the FaceForensics++ dataset is introduced in this work. A Convolutional Neural Network (CNN) based architecture is enhanced with vision Transformer encoders for identifying facial manipulations introduced at microscopic precision. The hybrid model integrates CNNs and ViTs, allowing for the capturing of both local and global features that help in detecting subtle manipulations. ViTs have a built-in self-attention mechanism, which allows the model to concentrate on essential facial characteristics, thereby enhancing precision even if the manipulations are confirmed in less than optimal situations. Additionally, the pre-trained Gemini 1.5 Pro model is fine-tuned for optimal performance. The classification of faces into authentic or fake is accurately done using the key points by 90%accuracy as shown through experimentation. Moreover, the Haar cascade method is used for human face detection technique as part of integrated design which augments its complete application in real-world functionalities. © 2025 IEEE.

关键词： Convolutional neural networks

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Plant Diseases Detection Using Deep Learning and Machine vision 3

Plant Diseases Detection Using Deep Learning and Machine Vis...

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3rd IEEE international conference on Intelligent Techniques in control, Optimization and Signal Processing, INCOS 2024

作者： Singh, Nidhi School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9798350361186

This research delves into deep learning and machine vision applications for plant leaf disease detection in agricultural settings, focusing on farm village datasets. Utilizing a blend of authentic farm village data and synthetic data from Generative Adversarial Networks (GANs), three advanced convolutional neural network (CNN) models VGG16, ResNet50, and InceptionNet V3 are employed with transfer learning. Leveraging transfer learning enhances model performance through fine-tuning pre-trained networks. The study systematically evaluates models based on key metrics like accuracy, precision, recall, and F1 score. Results showcase the methodology's robustness, with ResNet50 emerging as the leading performer at 83.23%, contributing to precision agriculture's advancements with promising implications for sustainable farming and crop yield optimization. © 2024 IEEE.

关键词： Generative adversarial networks

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Leveraging Swin Transformer for Local-to-Global Weakly Supervised Semantic Segmentation 13

Leveraging Swin Transformer for Local-to-Global Weakly Super...

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13th Iranian/3rd international Machine vision and Image Processing conference (MVIP)

作者： Ahmadi, Rozhan Kasaei, Shohreh Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

In recent years, weakly supervised semantic segmentation using image-level labels as supervision has received significant attention in the field of computer vision. Most existing methods have addressed the challenges arising from the lack of spatial information in these labels by focusing on facilitating supervised learning through the generation of pseudolabels from class activation maps (CAMs). Due to the localized pattern detection of Convolutional Neural Networks (CNNs), CAMs often emphasize only the most discriminative parts of an object, making it challenging to accurately distinguish foreground objects from each other and the background. Recent studies have shown that vision Transformer (ViT) features, due to their global view, are more effective in capturing the scene layout than CNNs. However, the use of hierarchical ViTs has not been extensively explored in this field. This work explores the use of Swin Transformer by proposing "SWTformer" to enhance the accuracy of the initial seed CAMs by bringing local and global views together. SWTformer-V1 generates class probabilities and CAMs using only the patch tokens as features. SWTformer-V2 incorporates a multi-scale feature fusion mechanism to extract additional information and utilizes a background-aware mechanism to generate more accurate localization maps with improved cross-object discrimination. Based on experiments on the PascalVOC 2012 dataset, SWTformer-V1 achieves a 0.98% mAP higher localization accuracy, outperforming state-of-the-art models. It also yields comparable performance by 0.82% mIoU on average higher than other methods in generating initial localization maps, depending only on the classification network. SWTformer-V2 further improves the accuracy of the generated seed CAMs by 5.32% mIoU, further proving the effectiveness of the local-to-global view provided by the Swin transformer. Code available at: https://***/RozhanAhmadi/SWTformer

关键词： Weakly Supervised Semantic Segmentation Class Activation Map Hierarchical vision Transformer Image-level label

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A Performance Comparison of Multiple Deep Learning Methodologies in Image to Text Synthesis Tasks 3

A Performance Comparison of Multiple Deep Learning Methodolo...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Martinez, Jude Joseph Lamug Winata, Edward Kevin Bina Nusantara International University Computer Science Department Faculty of Computing and Media Central Jakarta11480 Indonesia

ISBN: (纸本)9798331509675

Generating textual descriptions from visual inputs has become a critical area of research in the intersection of computer vision and natural language processing called image captioning. This study deep dives towards comparing a few popular image captioning methodologies, going from traditional encoder decoder architectures to more advanced techniques that incorporate attention mechanisms, transformers, and reinforcement learning. We evaluate the performance of these models on publicly available datasets containing logs from the online community Flickr on several dimensions including caption accuracy, diversity and computational efficiency. Captions are evaluated in terms of both quantitative metric(BLEU, ROUGE-N, METEOR, CIDEr, SPICE) and qualitative analysis(contextual relevance besides creativity) for the caption quality. The results show clear advantages and trade offs of each method, allowing for a holistic assessment of how each approach is suitable for real world problems. Thus, this work sheds important light on the trade offs between model complexity, efficiency and performance in image captioning tasks and provides a useful reference for researchers making similar decisions about model design in similar tasks. © 2025 IEEE.

关键词： Deep reinforcement learning

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