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Traffic sign detection with convolutional neural networks

Andreja Kovačič (2017) Traffic sign detection with convolutional neural networks. EngD thesis.

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    The goal of this thesis is to describe and use method Faster R-CNN for detection and recognition of traffic signs. It explores the possibility of using artificially generated images in validation set, in hopes of saving real images for train set. We tackle a real world problem of growing dataset through time. We'll try to find an optimal way to augment the already learned model with new images. Lastly, we try to apply a new method, online hard example mining, which is essentially bootstrapping for end-to-end systems.

    Item Type: Thesis (EngD thesis)
    Keywords: Faster R-CNN,classification,detection, online hard example mining, fine-tuning, traffic signs
    Number of Pages: 35
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    izr. prof. dr. Danijel Skočaj296Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537553859)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 3933
    Date Deposited: 13 Sep 2017 12:11
    Last Modified: 29 Sep 2017 11:12
    URI: http://eprints.fri.uni-lj.si/id/eprint/3933

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