# Synthetic Data for Object Classification in Industrial Applications

arXiv: https://arxiv.org/abs/2212.04790 · HTML: https://mlsanity.com/paper/2212.04790/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Understanding Neural Networks Through Deep Visualization](https://mlsanity.com/paper/1506.06579/) (2015)
- [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018)
- [UnrealCV: Connecting Computer Vision to Unreal Engine](https://mlsanity.com/paper/1609.01326/) (2016)
- [Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors](https://mlsanity.com/paper/2007.07396/) (2020)
- [Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks](https://mlsanity.com/paper/1706.07842/) (2017)
- [Ship classification from overhead imagery using synthetic data and domain adaptation](https://mlsanity.com/paper/1905.03894/) (2019)
- [Comparative Analysis of Multiple Deep CNN Models for Waste Classification](https://mlsanity.com/paper/2004.02168/) (2020)
- [Detection and Classification of Industrial Signal Lights for Factory Floors](https://mlsanity.com/paper/2004.11187/) (2020)
- [Fruit and Vegetable Identification Using Machine Learning for Retail Applications](https://mlsanity.com/paper/1810.09811/) (2018)
- [Classification of PS and ABS Black Plastics for WEEE Recycling Applications](https://mlsanity.com/paper/2110.12896/) (2021)

## Built on by (most cited first, 2 of 2)

- [Synthetic Data Generation for Bridging Sim2Real Gap in a Production Environment](https://mlsanity.com/paper/2311.11039/) (2023)
- [Analyzing mixed construction and demolition waste in material recovery facilities: evolution, challenges, and applications of computer vision and deep learning](https://mlsanity.com/paper/2409.13112/) (2024)

## Abstract (arXiv)

One of the biggest challenges in machine learning is data collection. Training data is an important part since it determines how the model will behave. In object classification, capturing a large number of images per object and in different conditions is not always possible and can be very time-consuming and tedious. Accordingly, this work explores the creation of artificial images using a game engine to cope with limited data in the training dataset. We combine real and synthetic data to train the object classification engine, a strategy that has shown to be beneficial to increase confidence in the decisions made by the classifier, which is often critical in industrial setups. To combine real and synthetic data, we first train the classifier on a massive amount of synthetic data, and then we fine-tune it on real images. Another important result is that the amount of real images needed for fine-tuning is not very high, reaching top accuracy with just 12 or 24 images per class. This substantially reduces the requirements of capturing a great amount of real data.