Abstract
Large image datasets are important for many different aspects of computer vision. However, creating datasets containing thousands or millions of labeled images is time consuming. Instead of manual collection of a large dataset, we propose a framework for generating large-scale datasets synthetically. Our framework is capable of generating realistic looking images with varying environmental conditions, while automatically creating labels. To evaluate usefulness of such a dataset, we generate two datasets containing vehicle images. Afterwards, we use these images to train a neural network. We then compare detection accuracy to the same neural network trained with images of existing datasets. The experiments show that our generated datasets are well-suited to train neural networks and achieve comparable accuracy to existing datasets containing real photographs, while they are much faster to create.
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Acknowledgment
This research is funded by the Bundesministerium für Wirtschaft und Energie as part of the TALAKO project [17] (grant number 01MZ19002A). The authors wish to thank Maximilian Fischer for implementing a prototypical system during his thesis.
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Roch, P., Shahbaz Nejad, B., Handte, M., Marrón, P.J. (2022). GUILD - A Generator for Usable Images in Large-Scale Datasets. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13599. Springer, Cham. https://doi.org/10.1007/978-3-031-20716-7_19
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