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From Synthetic Images Towards Detectors of Real Objects: A Case Study on Road Sign Detection

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Artificial Intelligence and Soft Computing (ICAISC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11509))

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Abstract

Large amounts of suitably marked images are required to feed a learning algorithm when building a detector. The process of collecting images and then marking locations of target objects in them is arduous. One could potentially speed up this process if real images for a given application were replaced by images generated synthetically, and coordinates of targets were simply imposed, rather then discovered manually. Despite the appeal of such an automatization, questions arise regarding the usefulness of systems built this way in real operating conditions. In particular, the obvious violation of i.i.d. principle might result in higher error rates at the testing stage.

In this paper we provide an experimental study of the above approach, taking up road sign detection as an example. We generate synthetic training scenes by laying road sign icons randomly over a set of backgrounds with additional perturbations (rotations, brightness changes, blurring, sharpening, noise). Ensemble learning is then carried out using a RealBoost algorithm with shallow decision trees. Haar-like features or Fourier moments constitute the direct input information extracted from images. In both cases we support the computations with suitable integral images. Accuracy of resulting detectors is finally tested on real images.

This work was financed by the National Science Centre, Poland. Research project no.: 2016/21/B/ST6/01495.

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Notes

  1. 1.

    As regards the negative examples, they usually do not have to be processed manually. One can implement an automatic sampling procedure that picks negative windows on random e.g. from images containing no targets.

  2. 2.

    Probably Approximately Correct.

  3. 3.

    An input image has to be scanned with a sliding window at several scales. Commonly, more than \(10^4\) window positions are checked for typical settings.

  4. 4.

    Templates can be scaled (stretched or squeezed) along either axis.

  5. 5.

    \(\langle a, b \rangle =\mathop {\sum \sum }\nolimits _{x,y} a(x,y) \overline{b}(x,y)\), where \(\overline{b}\) denotes the complex conjugate of b.

  6. 6.

    The moment of order zero is a real number: .

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Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, ul. Żołnierska 49, 71-210, Szczecin, Poland

    Przemysław Klęsk

Authors
  1. Przemysław Klęsk
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Correspondence to Przemysław Klęsk .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Klęsk, P. (2019). From Synthetic Images Towards Detectors of Real Objects: A Case Study on Road Sign Detection. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_2

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  • DOI: https://doi.org/10.1007/978-3-030-20915-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20914-8

  • Online ISBN: 978-3-030-20915-5

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