Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Synthetic Data Generation to Speed-up the Object Recognition Pipeline

Version 1 : Received: 22 November 2021 / Approved: 24 November 2021 / Online: 24 November 2021 (08:53:00 CET)

A peer-reviewed article of this Preprint also exists.

Perri, D.; Simonetti, M.; Gervasi, O. Synthetic Data Generation to Speed-Up the Object Recognition Pipeline. Electronics 2022, 11, 2. Perri, D.; Simonetti, M.; Gervasi, O. Synthetic Data Generation to Speed-Up the Object Recognition Pipeline. Electronics 2022, 11, 2.


This paper provides a methodology for the production of synthetic images for training neural networks to recognise shapes and objects. There are many scenarios in which it is difficult, expensive and even dangerous to produce a set of images that is satisfactory for the training of a neural network. The development of 3D modelling software has nowadays reached such a level of realism and ease of use that it seemed natural to explore this innovative path and to give an answer regarding the reliability of this method that bases the training of the neural network on synthetic images. The results obtained in the two proposed use cases, that of the recognition of a pictorial style and that of the recognition of migrants at sea, leads us to support the validity of the approach, provided that the work is conducted in a very scrupulous and rigorous manner, exploiting the full potential of the modelling software. The code produced, which automatically generates the transformations necessary for the data augmentation of each image, and the generation of random environmental conditions in the case of Blender and Unity3D software, is available under the GPL licence on GitHub. The results obtained lead us to affirm that through the good practices presented in the article, we have defined a simple, reliable, economic and safe method to feed the training phase of a neural network dedicated to the recognition of objects and features, to be applied to various contexts.


n/a; Unity3D; Blender; Virtual Reality; Syntetic dataset generation; Machine Learning; Neural Networks


Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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