Feature selection for surface defect classification of extruded aluminum profiles

Apostolos Chondronasios, Ivan Eugeniev Popov, Ivan Nikolov Jordanov

Research output: Contribution to journalArticlepeer-review


This research investigates detection and classification of two types of the surface defects in extruded aluminium profiles; blisters and scratches. An experimental system is used to capture images and appropriate statistical features from a novel technique based on gradient-only co-occurrence matrices (GOCM) are proposed to detect and classify three distinct classes; non-defective, blisters and scratches. The developed methodology makes use of the Sobel edge detector to obtain the gradient magnitude of the image (GOCM). A comparison is made between the statistical features extracted from the original image (GLCM) and those extracted from the gradient magnitude (GOCM). This paper describes in detail every step of the image processing with example pictures illustrating the methodology. The features extracted from the image processing are classified by a two-layer feed-forward artificial neural network. The artificial neural network training is tested using different combinations of statistical features with different topologies. Features are compared individually and grouped. Results are discussed, achieving up to 98.6 % total testing accuracy.
Original languageEnglish
Pages (from-to)33-41
Number of pages9
JournalInternational Journal of Advanced Manufacturing Technology
Issue number1
Early online date19 Jul 2015
Publication statusPublished - Mar 2016


  • Aluminum surface
  • Neural network
  • Co-occurrence matrix
  • Image gradient
  • Defect classification
  • Machine Learning


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