We propose an approach to generate synthetic data to train computer vision (CV) models for industrial wear and tear detection. Wear and tear detection is an important CV problem for predictive maintenance tasks in any industry. However, data curation for training such models is expensive and time-consuming due to the unavailability of datasets for different wear and tear scenarios. Our approach employs a vision language model along with a 3D simulation and rendering engine to generate synthetic data for varying rust conditions. We evaluate our approach by training a CV model for rust detection using the generated dataset and tested the trained model on real images of rusted industrial objects. The model trained with the synthetic data generated by our approach, outperforms the other approaches with a mAP50 score of 0.87. The approach is customizable and can be easily extended to other industrial wear and tear detection scenarios
@article{arxiv.2509.04894,
title = {SynGen-Vision: Synthetic Data Generation for training industrial vision models},
author = {Alpana Dubey and Suma Mani Kuriakose and Nitish Bhardwaj},
journal= {arXiv preprint arXiv:2509.04894},
year = {2025}
}