Essays about: "PSPNet"

Found 3 essays containing the word PSPNet.

  1. 1. Screw Hole Detection in Industrial Products using Neural Network based Object Detection and Image Segmentation : A Study Providing Ideas for Future Industrial Applications

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Jakob Melki; [2022]
    Keywords : Artificial intelligence AI ; Automated assembly and disassembly; Computer vision; Machine learning; Neural networks; Object detection; Screw hole detection; Semantic segmentation; Artificiell intelligens AI ; Automatiserad montering och demontering; Datorseende; Maskininlärning; Neurala nätverk; Objekt detektering; Skruvhålsdetektering; Semantisk segmentering;

    Abstract : This project is about screw hole detection using neural networks for automated assembly and disassembly. In a lot of industrial companies, such as Ericsson AB, there are products such as radio units or filters that have a lot of screw holes. READ MORE

  3. 2. Detecting Slag Formation with Deep Learning Methods : An experimental study of different deep learning image segmentation models

    University essay from Linköpings universitet/Datorseende

    Author : Christian von Koch; William Anzén; [2021]
    Keywords : deep learning; deep neural network; computer vision; image segmentation; iron ore pelletising plant; furnace slag-detection; U-Net; PSPNet;

    Abstract : Image segmentation through neural networks and deep learning have, in the recent decade, become a successful tool for automated decision-making. For Luossavaara-Kiirunavaara Aktiebolag (LKAB), this means identifying the amount of slag inside a furnace through computer vision. READ MORE

  4. 3. Semantic Segmentation of Iron Ore Pellets with Neural Networks

    University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknik

    Author : Terese Svensson; [2019]
    Keywords : Convolutional Neural Networks; Iron Ore Pellets; Semantic Segmentation;

    Abstract : This master’s thesis evaluates five existing Convolutional Neural Network (CNN) models for semantic segmentation of optical microscopy images of iron ore pellets. The models are PSPNet, FC-DenseNet, DeepLabv3+, BiSeNet and GCN. READ MORE