Type: Conference paper
Detection and classification of surface defects of cold rolling mill steel using morphology and neural network
Journal: 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 ()Year: 2008Volume: Issue: Pages: 1071 - 1076
Yazdchi M.aGolibagh Mahyari A. Nazeri A.Yazdchi M.aYazdchi M.aYazdchi M.aGolibagh Mahyari A.Golibagh Mahyari A.Golibagh Mahyari A. Nazeri A. Nazeri A.
DOI:10.1109/CIMCA.2008.130Language: English
Abstract
As manufacturing speed increases in the steel industry, fast and exact product inspection becomes more important. This paper deals with defect detection and classification algorithm for high-speed steel bar in coil. We enhance an acquired image by use of a special subtractive method and find the position of defect using local entropy and morphology. The extracted statistical features are then presented to a classifier. We use neural network and fuzzy inference system as a classifier and compare their results. The best accuracy, % 97.19, is obtained by the neural network. © 2008 IEEE.
Other Keywords
ClassifiersCold rollingCold rolling millsFuzzy inferenceInspectionIron and steel plantsLearning systemsMorphologyNeural networksClassification algorithmDefect detectionFuzzy inference systemsHigh-speed steelsLocal entropyProduct inspectionSpeed increaseStatistical featuresSteel industrySubtractive methodSurface defects