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Detecting Objects for Autonomous System Verificatio

Result description

In this thesis we created a framework for easy evaluation and training of Faster R-CNN type of networks. We fine-tuned VGG16 and ZFNet networks on our internal Victims dataset as well as standard KITTI dataset. We later showed that VGG16 architecture is far more suitable for fine-tuning on data from slightly different training and target domains. This framework can later serve as a baseline for further improvements in the field.

Keywords

Detecting ObjectsAutonomous System Verificatio

The result's identifiers

Alternative languages

  • Result language

    angličtina

  • Original language name

    Detecting Objects for Autonomous System Verificatio

  • Original language description

    In this thesis we created a framework for easy evaluation and training of Faster R-CNN type of networks. We fine-tuned VGG16 and ZFNet networks on our internal Victims dataset as well as standard KITTI dataset. We later showed that VGG16 architecture is far more suitable for fine-tuning on data from slightly different training and target domains. This framework can later serve as a baseline for further improvements in the field.

  • Czech name

  • Czech description

Classification

  • Type

    Vsouhrn - Summary research report

  • CEP classification

    JD - Use of computers, robotics and its application

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    R - Projekt Ramcoveho programu EK

Others

  • Publication year

    2016

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Number of pages

    60

  • Place of publication

    Praha

  • Publisher/client name

    ČVUT FEL, Katedra kybernetiky - Centrum strojového vnímání

  • Version

Basic information

Result type

Vsouhrn - Summary research report

Vsouhrn

CEP

JD - Use of computers, robotics and its application

Year of implementation

2016