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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

共 3294 条记录，以下是11-20 订阅

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Advancing Deep Learning on Edge Devices: Fine-Tuning and Deployment of YOLOv7 Model for Efficient Object Detection in AI based Computer vision applications 3

Advancing Deep Learning on Edge Devices: Fine-Tuning and Dep...

引用

3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shekhar, Sudhanshu Sathwik, T.S. Pritwani, Mayank Mohana Ramakanth Kumar, P. Sreelakshmi, K. RV College of Engineering® Bengaluru India

ISBN: (纸本)9798331527549

This paper investigates the optimization and deployment of YOLOv7 deep learning model on NviDIA Jetson Nano, an AI-focused edge computing platform for object detection in various computer vision applications. The work leverages TensorRT and quantization techniques for model acceleration for good detection accuracy. Further it examines performance metrics such as speed, accuracy, and resource utilization for image dataset. The model is trained using 80 different classes of objects and demonstrates the use of 6 classes. The average detection accuracy obtained 92.35% and the average processing time is 117.8ms. This work supports AI by demonstrating the feasibility of running deep learning models on edge devices and provides insight into the challenges and opportunities of optimizing AI models for energy-efficient, real-time operations on edge devices for various computer vision applications. © 2025 IEEE.

关键词： Adversarial machine learning

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image Generation from Arabic Text: Comparative Study of Proposed Architectures 8th

Image Generation from Arabic Text: Comparative Study of Prop...

引用

8th International conference on Arabic Language processing

作者： Bahani, Mourad El Ouaazizi, Aziza Maalmi, Khalil Essahlaoui, Abdelouahed Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Artificial Intelligence Data Sci & Emerging Syst Fes Morocco Sidi Mohamed Ben Abdellah Univ Lab Engn Sci FPT Taza Morocco

ISBN: (纸本)9783031804373;9783031804380

Text-to-image generation is a cutting-edge technology that enables computers to generate images from textual descriptions. While this technology has been extensively researched and applied to English language text, applying it to Arabic language text is still in its early stages. Additionally, the Arabic language is challenging due to its right-to-left writing system and extensive vocabulary of 1.3 million words. In this paper, we explore text-to-image generation for generating images from Arabic language text descriptions. Firstly, we fine-tune a transformer-based model pre-trained on the Arabic text to transform the text information into affine transformation within the DF-GAN generator. Secondly, we present a text transformer that combines LSTM layers to address the limitation of unrecognized words. Thirdly, a mask predictor is trained into the generator using a weakly supervised method and incorporated into the affine transformation for a more effective integration of image and text features. In addition, we add the DAMSM loss function as a regularization to the loss function to achieve convergences and stability in the training phase. The experiment on two challenging datasets CUB and Oxford-flower shows that our architectures can accurately generate high-quality images faithfully representing the Arabic textual descriptions. We believe the scaling of this task could have critical applications in fields such as Arabic visual learning, e-commerce, advertising, and entertainment.

关键词： machine Learning Deep Learning Computer vision Generative Adversarial Networks Text-to-image Generation Arabic Text processing

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vision-Based Autonomous Car Brake and Steering Assistance Using Deep Learning 1

Vision-Based Autonomous Car Brake and Steering Assistance Us...

引用

1st International conference on AIML-applications for Engineering and Technology, ICAET 2025

作者： Anees, H. Agrawal, Pooja School of Robotics Defence Institute of advanced technology Pune India

ISBN: (纸本)9798350355611

This paper presents a novel approach for enhancing vehicle safety and navigation through an integrated system for lane detection, vehicle alignment, and automatic braking using visual feedback. Our proposed system employs advanced deep learning and computer vision techniques with real-time processing to detect the exact boundaries of lane and ensures precise vehicle movement within the lane. The system continuously analyses lane markings and modifies the vehicle's position to ensure optimal lane adherence by utilizing a combination of machine learning algorithms and camera-based image processing. Additionally, the system incorporates an adaptive braking mechanism that identifies vehicles ahead using visual inputs. Furthermore, the jerks experienced during steering alignment can be greatly reduced by the suggested steering control system. The system's efficiency in various driving conditions is evidenced by its experimental simulation results, which also show improvements in collision avoidance and lane-keeping accuracy. This approach contributes to improved driving convenience and road safety by marking a substantial advancement in autonomous driving technologies. © 2025 IEEE.

关键词： Steering

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An Experimental Evaluation of LLM on image Classification 35th

An Experimental Evaluation of LLM on Image Classification

引用

35th Australasian Database conference

作者： Wu, Jiaxuan Tang, Xushuo Yang, Zhengyi Hao, Kongzhang Lai, Longbin Liu, Yongfei Univ Calif Irvine Irvine CA 92697 USA Euler AI Sydney NSW Australia Univ New South Wales Sydney NSW Australia Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9789819612413;9789819612420

image classification is one of the fundamental tasks in computer vision (CV) and has numerous practical applications. Traditionally, machine learning and deep learning methods such as k-Nearest Neighbors (kNN), decision trees, and Convolutional Neural Networks (CNN) have been widely used to perform this task. However, with the recent emergence of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), originally designed for natural language processing, their cross-domain applications, including in CV, are now being explored. In this paper, we investigate the capabilities of GPT-4o, a variant of the GPT model, for image classification on the Fashion-MNIST dataset. By using carefully designed prompts, we evaluate GPT-4o's performance and compare it with more traditional models. Our study offers insights into the cross-domain potential of GPT models, explores how prompt engineering can enhance GPT's performance on image classification tasks, and suggests new avenues for developing more flexible and adaptable multimodal LLM systems. The code can be found at https://***/Tanghaha1424/gpt-fashionmnist.

关键词： LLM ChatGPT image Classification Fashion-MNIST

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Road Infrastructure Defect Detection using Yolo8Seg Based Approach 6

Road Infrastructure Defect Detection using Yolo8Seg Based Ap...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Alsubaie, Norah A. Almalki, Ghayda A. Almutairi, Ghada N. Alrumaih, Sarah A. Princess Nourah Bint Abdulrahman University Department of Computer Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798331506520

This research introduces "Jaddah,"an innovative AI-based system for the automated detection of road infrastructure defects using advanced computer vision and machine learning techniques. The system addresses the limitations of traditional road inspection methods, which are often slow and prone to human error. Jaddah develops a mobile application that efficiently detects, classifies, and segments road defects at the pixel level. By utilizing a comprehensive dataset of high-resolution images, the model training process is significantly enhanced. The YOLOv8-seg model is implemented to achieve precise defect localization and segmentation, ensuring high accuracy in identifying and categorizing road defects. Performance metrics show an impressive 87% mAP50, demonstrating reliable defect detection. These results contribute to improved infrastructure maintenance, enhanced road safety, and greater operational efficiency. © 2025 IEEE.

关键词： image enhancement

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A Comparative Study of Deep Learning Algorithms for Glaucoma Classification Using Retinal images 5th

A Comparative Study of Deep Learning Algorithms for Glaucoma...

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5th International conference on Data Science, machine Learning and applications

作者： Swapna, T. Varshitha, Y. Sai Raja Sudeepthi, K. L. Manavika, B. Saishree, T. G Narayanamma Inst Technol & Sci Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

A group of eye conditions known as glaucoma impair the optic nerve, which is in charge of sending visual data from the eye to the brain. Glaucoma impacts 3.54% of adults aged 40 to 80 around the world. Early detection of glaucoma is crucial as it can prevent total optic nerve damage, which would cause irreversible vision loss. It is possible for specialists to diagnose glaucoma medically, but treatment options are either expensive or time-consuming and requires ongoing care from medical professionals. There have been numerous initiatives at streamlining all components of the glaucoma categorization process, however these models are challenging for users to comprehend the key predictors, resulting in them being unreliable for use by medical experts. The study uses eye fundus images to classify glaucoma patients using three distinct Deep Learning techniques: Convolutional neural network, visual Geometry Group 16 (VGG16), and Global Context Network (GC-Net). In addition, several data pre-processing techniques are used to avoid overfitting and achieve high accuracy. This research compares and analyses the performance of various architectures using the aforementioned techniques. The CNN model had the best accuracy of 83% when in contrast to the other deep learning models.

关键词： Biomedical image processing Glaucoma Blind ness machine learning visual geometry group 16 Convolutional neural network Global context network

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5th International conference on Data Science, machine Learning and applications, ICDSMLA 2023

5th International Conference on Data Science, Machine Learni...

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5th International conference on Data Science, machine Learning and applications, ICDSMLA 2023

ISBN: (纸本)9789819780426

The proceedings contain 128 papers. The special focus in this conference is on Data Science, machine Learning and applications. The topics include: Digitization of Monuments – An Impact on the Tourist Experience with Special Reference to Hampi;resume Parser Using machine Learning;IOT Based Smart Hydroponics System;comparative Study of machine Learning and Deep Learning Techniques for Cancer Disease Detection;High Thruput Modulation Approaches Used in Next Generation WiF’s Under Multi-impairments Environments with MATLAB Codes;skin Disease Detection;root Vegetable Crop Recommendation System Based on Soil Properties and Environmental Factors;deep Learning Model Development for an Automatic Healthcare Edge Computing Application;Empathetic Conversations in Mental Health: Fine-Tuning LLMs for Supportive AI Interactions;exploring Block Chain Technology with applications, and Future Prospects;a Comprehensive Review of Soft Computing Enabled Techniques for IoT Security: State-of-the-Art and Challenges Ahead;Performance Analysis of machine Learning Algorithms on Imbalanced Datasets Using SMOTE Technique;An AI Based Nutrient Tracking and Analysis System;power Saving Mechanism for Street Lights System Using IoT;Automatic Login System Using ATTINY85 IC;forecasting Stock Prices: A Comparative Analysis of machine Learning, Deep Learning, and Statistical Approaches;smart vision Bot;robots in Logistics: Apprehension of Current Status and Future Trends in Indian Warehouses;smart Healthcare: Enhancing Patient Well-Being with IoT;Detection of B-ALL Using CNN Model and Deep Learning;a Comprehensive Analysis for Advancements and Challenges in Deep Learning Models for image processing;a Comprehensive Survey on Enhancing Patient Care Through Deep Learning and IoT-Enabled Healthcare Innovations;attention-Based image Caption Generation.

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Wild Animal Detection System on a Farm Using image processing

Wild Animal Detection System on a Farm Using Image Processin...

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International conference on Advances and applications of Artificial Intelligence and machine Learning, ICAAAIML 2023

作者： Barhate, Madhuri Gulhane, Nandini Gunjal, Tanvi Gudi, Swaroop Gulhane, Vedant Wagade, Prasad Gude, Ayush Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9789819795062

Human civilization is based on agriculture, and one of the biggest threats to agricultural productivity is the existence of wild animals on farmlands. Animals are taking over some farmlands, causing significant crop losses, property damage, and livestock risks. As a result, suitable solutions must be developed. A system to support farmers is necessary because the conventional method of constant guard monitoring is impractical. Modern technologies like machine learning, computer vision, and image processing are used by this wild animal detection system to effectively manage conflicts between humans and wildlife. machine learning and computer vision rely on the integration of particular algorithms to guarantee the accuracy and effectiveness of the system. With an emphasis on sustainability, the technology protects property and crops while also aiding in the conservation of wildlife. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Livestock

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5th International conference on Data Science, machine Learning and applications, ICDSMLA 2023

5th International Conference on Data Science, Machine Learni...

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5th International conference on Data Science, machine Learning and applications, ICDSMLA 2023

ISBN: (纸本)9789819780303

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3rd International conference on machine vision and Augmented Intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

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3rd International conference on machine vision and Augmented Intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on machine vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep Learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral image Classification: A Comparative Analysis;deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI images Using Deep Learning;software Maintenance Prediction Using Stack Ensemble Deep Learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic applications;shaft Rotation Monitoring Using Radar Signal processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus images Using Pyramid Real image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare Data;multimodal Face Recognition System Using Hybrid Deep Learning Feature;Classification of Copy and Move image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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