MACHINE LEARNING SYSTEM BASED ON COMPUTER VISION FOR THE AUTOMATIC INSPECTION OF MAGNETIC PARTICLES IN MARINE STRUCTURES
| Autor: | Pedro Javier Navarro Lorente, Ignacio Jesus Moreo Lopez |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science 020209 energy Supervised learning 0211 other engineering and technologies General Engineering Pattern recognition 02 engineering and technology HSL and HSV Color space Set (abstract data type) Support vector machine Naive Bayes classifier Software 021105 building & construction 0202 electrical engineering electronic engineering information engineering RGB color model Artificial intelligence business |
| Zdroj: | DYNA. 93:636-642 |
| ISSN: | 1989-1490 |
| DOI: | 10.6036/8820 |
| Popis: | This work presents a system of supervised learning based on computer vision with the aim of solving the automation of non-destructive inspection tests based on magnetic particles. In this paper, three supervised learning algorithms have been tested: the nearest k neighbor (kNN), a Bayesian classifier (NBC) and the vector support machine (SVM). The developed system has been successfully tested on a set of images extracted during the inspection of magnetic particles on marine structures at the Navantia shipyard in Cartagena. The algorithm that offered the best result was the SVM with a sensitivity of 98.6% and a specificity of 100.0% in the detection of faults by magnetic particles. The vector of characteristics used is composed of a set of 16 elements formed by geometric characteristics and intensity values of the RGB, HSV, and CIE L * a * b * color spaces. The work presents a software application and a hardware system that, using the SVM algorithm, is capable of automatically detecting defects on marine structures during the magnetic particle test. Keywords. Magnetic particles, Non-destructive testing, Machine learning, Computer vision |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|