Work

Integrating Computer Vision Into Equipment Maintenance at Chu-ufpa

Year: 2025

Type: conference-paper

Abstract: This study applies computer vision techniques to detect defects in printed circuit boards of multiparameter monitors and pulmonary ventilators, in order to reduce corrective maintenance. Using transfer learning and data augmentation, the model achieved 77.77% accuracy, despite limited real data.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17363836

Authors: Oliveira, Silvana da Costa, da Luz, Valdemir Rodrigues, Müller, Ana Carolina Q. S.

Institution: Universidade Federal do Pará

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W7091307216

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