Deep learning for segmentation and classification of rock grains in the aggregates industry - Nunes, Ricardo Ramos; Silva, Renato Moraes (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2024.245219, 2024)
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.