Learning Safety Equipment Detection using Virtual Worlds

@article{DiBenedetto2019LearningSE,
  title={Learning Safety Equipment Detection using Virtual Worlds},
  author={Marco Di Benedetto and Enrico Meloni and Giuseppe Amato and F. Falchi and Claudio Gennaro},
  journal={2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
  year={2019},
  pages={1-6}
}
Nowadays, the possibilities offered by state-of-the-art deep neural networks allow the creation of systems capable of recognizing and indexing visual content with very high accuracy. Performance of these systems relies on the availability of high quality training sets, containing a large number of examples (e.g. million), in addition to the the machine learning tools themselves. For several applications, very good training sets can be obtained, for example, crawling (noisily) annotated images… Expand
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