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Advisor(s)
Abstract(s)
In the rapidly evolving field of machine learning engineering, access to large, high-quality, and well-balanced labeled datasets is indispensable for accurate product classification. This necessity holds particular significance in sectors such as the ceramics industry, in which effective production line activities are paramount and deep learning classification mechanisms are particularly relevant for streamlining processes; but real-world image samples are scarce and difficult to obtain, hindering dataset building and consequently model training and deployment. This paper presents a novel approach for dataset building in the context of the ceramic industry, which involves employing synthetic images for building or complementing datasets for image classification problems. The proposed methodology was implemented in CeramicFlow, an innovative computer graphics rendering pipeline designed to create synthetic images by employing computer-aided design models of ceramic objects and incorporating domain randomization techniques. As a result, a fully synthetic image dataset named Synthetic CeramicNet was created and validated in real-world ceramic classification problems. The results demonstrate that synthetic images provide an adequate basis for datasets and can significantly reduce reliance on real-world data when developing deep learning approaches for image classification problems in the ceramic industry. Furthermore, the proposed approach can potentially be applied to other industrial fields.
Description
Article number - 110019
Keywords
Ceramic industry Computer vision Deep learning Image classification Synthetic data
Citation
Fábio Gaspar, Daniel Carreira, Nuno Rodrigues, Rolando Miragaia, José Ribeiro, Paulo Costa, António Pereira, Synthetic image generation for effective deep learning model training for ceramic industry applications, Engineering Applications of Artificial Intelligence, Volume 143, 2025, 110019, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2025.110019
Publisher
Elsevier