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Automatic Defect Detection and Classification of Terminals in a Bussed Electrical Center Using Computer Vision
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Author(s): Osslan Osiris Vergara Villegas (Universidad Autónoma de Ciudad Juárez, Mexico), Vianey Guadalupe Cruz Sánchez (Universidad Autónoma de Ciudad Juárez, Mexico), Humberto de Jesús Ochoa Domínguez (Universidad Autónoma de Ciudad Juárez, Mexico), Jorge Luis García-Alcaraz (Universidad Autónoma de Ciudad Juárez, Mexico)and Ricardo Rodriguez Jorge (Universidad Autónoma de Ciudad Juárez, Mexico)
Copyright: 2016
Pages: 26
Source title:
Handbook of Research on Managerial Strategies for Achieving Optimal Performance in Industrial Processes
Source Author(s)/Editor(s): Giner Alor-Hernández (Instituto Tecnológico de Orizaba, Mexico), Cuauhtémoc Sánchez-Ramírez (Instituto Tecnológico de Orizaba, Mexico)and Jorge Luis García-Alcaraz (Universidad Autónoma de Ciudad Juárez, Mexico)
DOI: 10.4018/978-1-5225-0130-5.ch012
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Abstract
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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