Detection of Irregularities on Automotive Semiproducts

  • Erik Dovgan, Klemen Gantar, Valentin Koblar, and Bogdan Filipič. Detection of Irregularities on Automotive Semiproducts. In Proceedings of the 17th International Multiconference Information Society (IS 2014), pages 22-25, Ljubljana, Slovenia, 2014.
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    The use of applications for automated inspection of semiproducts is increasing in various industries, including the automotive industry. This paper presents the development of an application for automated visual detection of irregularities on commutators that are parts of vehicle’s fuel pumps. Each type of irregularity is detected on a partition of the commutator image. The initial results show that such an automated inspection is able to reliably detect irregularities on commutators. In addition, the results confirm that the set of attributes used to build the classifiers for detecting individual types of irregularities and the priority of these classifiers significantly influence the classification accuracy.

    @InProceedings{2014-10-DOVGAN,
    author = {Erik Dovgan and Klemen Gantar and Valentin Koblar and Bogdan Filipi\v{c}},
    title = {{Detection of Irregularities on Automotive Semiproducts}},
    booktitle = {{Proceedings of the 17th International Multiconference Information Society (IS 2014)}},
    date = {2014-10-06/2014-10-10},
    pages = {22-25},
    address = {Ljubljana, Slovenia},
    url = {http://www.copcams.eu/wp-content/uploads/2014/10/Dovgan_etal_IS2014_Vol.A_22-25.pdf},
    abstract = {The use of applications for automated inspection of semiproducts is increasing in various industries, including the automotive industry. This paper presents the development of an application for automated visual detection of irregularities on commutators that are parts of vehicle’s fuel pumps. Each type of irregularity is detected on a partition of the commutator image. The initial results show that such an automated inspection is able to reliably detect irregularities on commutators. In addition, the results confirm that the set of attributes used to build the classifiers for detecting individual types of irregularities and the priority of these classifiers significantly influence the classification accuracy.},
    year = {2014}
    }

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