Repositorio Académico Institucional

Automated inspection processes offer the advantages of objectivity and efficiency for each test. For this reason, certain techniques for digital image processing and machine vision have been developed. However, it remains an open question due to the great variability in appearance and shape of the objects of study. In this work, we propose an inspection methodology that allows us to analyze objects for which we have developed an active inspection system to detect damage or malfunction. Its operation begins with the acquisition of images of the object using two cameras located in front of a rotating platform, with a controlled rotation, and projecting a laser line on the object. The following is the 3D reconstruction of a section of the object, to then make a comparison using a 3D descriptor and distance analysis between points of the 3D test model and the 3D model generated with a base version of the same object without damage, which was stored in a database. If any damage is detected, the inspection is continued until the entire damaged section is completed and its volume is calculated. The main contributions of our work are summarized as: (i) Efficient combination of machine vision algorithms for volume estimation, (ii) Active inspection in the automatic search for surface damage on objects, and (iii) Measurement of the damage volume detected in metric units, which can be subtractive or additive damage. To validate our proposal, we have applied this methodology to different objects, thus being able to estimate the volume of damage (if any). Our results are promising, as we achieved a Mean Absolute Percentage Error, MAPE=2.78%. © 2022 Elsevier Ltd