DIE-VIS: An Automated Visual Inspection System for Cardboard Box Manufacturing
Abstract
In recent decades, the industrial world has undergone a significant transformation through the inclusion of innovative technologies that enhance manufacturing processes. In this context, Machine Vision inspection systems play a key role in ensuring quality by identifying defects in production. Automated defect detection systems improve productivity by reducing manual interventions, which can be time-consuming and prone to errors. This paper presents DIE-VIS, a real-world implemented visual inspection system for detecting defects in cardboard box manufacturing using traditional Computer Vision techniques. We provide a comprehensive evaluation comparing it to the YOLOv8 state-of-the-art deep learning model, demonstrating how, in the specific application of cardboard manufacturing, customized solutions still offer fundamental advantages.
Cite
Text
Monti et al. "DIE-VIS: An Automated Visual Inspection System for Cardboard Box Manufacturing." European Conference on Computer Vision Workshops, 2024. doi:10.1007/978-3-031-92805-5_17Markdown
[Monti et al. "DIE-VIS: An Automated Visual Inspection System for Cardboard Box Manufacturing." European Conference on Computer Vision Workshops, 2024.](https://mlanthology.org/eccvw/2024/monti2024eccvw-dievis/) doi:10.1007/978-3-031-92805-5_17BibTeX
@inproceedings{monti2024eccvw-dievis,
title = {{DIE-VIS: An Automated Visual Inspection System for Cardboard Box Manufacturing}},
author = {Monti, Flavia and Marinacci, Matteo and Leotta, Francesco and Mecella, Massimo},
booktitle = {European Conference on Computer Vision Workshops},
year = {2024},
pages = {259-275},
doi = {10.1007/978-3-031-92805-5_17},
url = {https://mlanthology.org/eccvw/2024/monti2024eccvw-dievis/}
}