Sökning: "CNN-modeller"

Visar resultat 1 - 5 av 17 uppsatser innehållade ordet CNN-modeller.

  1. 1. Object Recognition and Tracking of Bolts: A Comparative Analysis of CNN Models and Computer Vision Techniques : A Comparison of CNN Models and Tracking Algorithms

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Serhat Bulun; [2023]
    Nyckelord :;

    Sammanfattning : The newer generation industry 4.0 focuses on development of both flexibility and autonomy for power tools used by companies in different mechanical areas and assembly lines. One area for automation is the application of computer vision in power tools to detect, identify and track bolts. LÄS MER

  3. 2. Hybrid Deep Learning Model for Cellular Network Traffic Prediction : Case Study using Telecom Time Series Data, Satellite Imagery, and Weather Data

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Ali Shibli; [2022]
    Nyckelord :Cellular network traffic; multi-modal; satellite imagery; weather data; LSTM; CNN; time series; Trafic sur les réseaux cellulaires; multimodal; imagerie satellite; données météo; LSTM; CNN; séries temporelles; Förutsägelse av mobilnätstrafik; multimodal modell; satellitbilder; väderdata; LSTM; CNN; tidsseriein;

    Sammanfattning : Cellular network traffic prediction is a critical challenge for communication providers, which is important for use cases such as traffic steering and base station resources management. Traditional prediction methods mostly rely on historical time-series data to predict traffic load, which often fail to model the real world and capture surrounding environment conditions. LÄS MER

  4. 3. Classification and localization of extreme outliers in computer vision tasks in surveillance scenarios

    M1-uppsats, KTH/Hälsoinformatik och logistik

    Författare :Tariq Daoud; Emanuel Zere Goitom; [2022]
    Nyckelord :Computer vision; deep learning; machine learning; artificial neural networks; convolutional neural networks; YOLOv4; Datorseende; djupinlärning; maskininlärning; artificiella neurala nätverk; konvolutionella neurala nätverk; YOLOv4;

    Sammanfattning : Convolutional neural networks (CNN) have come a long way and can be trained toclassify many of the objects around us. Despite this, researchers do not fullyunderstand how CNN models learn features (edges, shapes, contours, etc.) fromdata. LÄS MER

  5. 4. Estimation of Water Depth from Multispectral Drone Imagery : A suitability assessment of CNN models for bathymetry retrieval in shallow water areas

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Qianyao Shen; [2022]
    Nyckelord :Bathymetry Retrieval; Multispectral Imagery; Convolutional Neural Network CNN ; Hämtning Av Batymetri; Multispektrala Bilder; Konvolutionellt Neuralt Nätverk CNN ;

    Sammanfattning : Aedes aegypti and Aedes albopictus are the main vector species for dengue disease and zika, two arboviruses that affect a substantial fraction of the global population. These mosquitoes breed in very slow-moving or standing pools of water, so detecting and managing these potential breeding habitats is a crucial step in preventing the spread of these diseases. LÄS MER

  7. 5. Identifying Melanoma Using Transfer Learning and Convolutional Neural Networks : An investigation of skin disease pre-training

    Kandidat-uppsats, KTH/Datavetenskap

    Författare :Albin Wikström Kempe; Elin Inoue; [2022]
    Nyckelord :;

    Sammanfattning : Melanoma, the deadliest form of skin cancer, has become an increasingly common and pressing health issue. Early detection and treatment can be life-saving, but also poses a challenge. A possible solution presents itself in Convolutional Neural Networks (CNNs). LÄS MER