Deep learning for segmentation and classification of rock grains in the aggregates industry

• Ricardo Ramos Nunes Centro Universitário Facens
• Renato Moraes Silva USP

DOI: https://doi.org/10.5753/eniac.2024.245219

Resumo

This paper describes the application of a convolutional neural network (CNN) to detect rock grains in aggregates that exceed a specified size. In civil construction, the quality of aggregates is crucial and generally assessed by granulometry, with traditional sieving methods being time-consuming and susceptible to human error. This study proposes using machine learning to measure grain size continuously during the production process. Using CNN with the U-net architecture, the models were trained to evaluate grain size in a simulated condition that reproduces the environment of a conveyor belt. The results indicate that the developed models have strong generalization capabilities and can effectively identify contamination by rock grains that exceed the permitted size.

Palavras-chave: U-Net architecture, semantic segmentation, rock grain segmentation

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