Automatic Defect Detection and Classification of Terminals in a Bussed Electrical Center Using Computer Vision

  1. Osslan Osiris Vergara Villegas 1
  2. Vianey Guadalupe Cruz Sánchez 1
  3. Humberto de Jesús Ochoa Domínguez 1
  4. Jorge Luis García-Alcaraz 1
  5. Ricardo Rodriguez Jorge 1
  1. 1 Universidad Autónoma de Ciudad Juárez
    info

    Universidad Autónoma de Ciudad Juárez

    Ciudad Juárez, México

    ROR https://ror.org/05fj8cf83

Libro:
Handbook of Research on Managerial Strategies for Achieving Optimal Performance in Industrial Processes

Editorial: IGI Global

ISSN: 2327-350X,  2327-3518

ISBN: 9781522501305,  9781522501312

Año de publicación: 2016

Páginas: 241-266

Tipo: Capítulo de Libro

DOI: 10.4018/978-1-5225-0130-5.CH012 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

In this chapter, an intelligent Computer Vision (CV) system, for the automatic defect detection and classification of the terminals in a Bussed Electrical Center (BEC) is presented. The system is able to detect and classify three types of defects in a set of the seven lower pairs of terminals of a BEC namely: a) twisted; b) damaged and c) missed. First, an environment to acquire a total of 56 training and test images was created. After that, the image preprocessing is performed by defining a Region Of Interest (ROI) followed by a binarization and a morphological operation to remove small objects. Then, the segmentation stage is computed resulting in a set of 12-14 labeled zones. A vector of 56 features is extracted for each image containing information of area, centroid and diameter of all terminals segmented. Finally, the classification is performed using a K-Nearest Neighbor (KNN) algorithm. Experimental results on 28 BEC images have shown an accuracy of 92.8% of the proposed system, allowing changes in brightness, contrast and salt and pepper noise.

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