Resumo:
Modern production systems require monitoring processes for quality assurance and production
compliance. Computer vision is a strong ally in the process of object identification
and feature extraction, being used in several fields of application. Extracting geometric
features from irregularly shaped objects is a challenge, especially due to the complexity of
the measurement system. Manual measurement methods may not guarantee the required
accuracy and also have the demand of an experienced operator, time and directly impact
the final cost of the product. Thus, the objective of this research is the creation of a computational
modeling for the extraction of geometric characteristics of objects of irregular
geometry, digitally, using computer vision. The standard block analysis confirmed that
the measurement system based on computational modeling is satisfactory and has better
accuracy, compared to the manual measurement method aided by image analyzer. The
analysis of the experiments defined as the object of study also confirmed the effectiveness
of the measurement system and it was possible to calculate the bias of the manual operator.
As a result, measurements on the object of study were successfully extracted, made
available in a systematic way, in record time. The method proved to be very effective,
opening the possibility for several future works in the area.
