Type: Conference paper
Steel surface defect detection using texture segmentation based on multifractal dimension
Journal: 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 ()Year: 2009Volume: Issue: Pages: 346 - 350
Yazdchi M.a Yazdi M.Golibagh Mahyari A.Yazdchi M.aYazdchi M.aYazdchi M.a Yazdi M. Yazdi M. Yazdi M.Golibagh Mahyari A.Golibagh Mahyari A.Golibagh Mahyari A.
DOI:10.1109/ICDIP.2009.68Language: English
Abstract
Recently, it becomes significant to enhance quality of products as well as to increase quantity of products in the steel manufacturing industry. As a manufacturing gets faster, the fast and exact detection of defect is important to acquire a competitive power. Without automatic machine vision technology, steel rolling operations is not able to perform realtime inline surface defect inspection. In this paper, we propose a new defect detection algorithm based on multifractal. Then, some suitable features are extracted and presented to neural network for classification. The obtained accuracy is % 97.9. © 2009 IEEE.
Author Keywords
DefectMorphologyMultifractalSteelTexture segmentation
Other Keywords
Computer visionDigital image storageFractalsImaging systemsInspectionMorphologyTexturesAutomatic machinesCompetitive powerDefect detectionDefect detection algorithmDefect inspectionDetection of defectsIn-line surfacesMulti fractalsMulti-fractal dimensionsMultifractalQuality of productReal timeRolling operationsSteel manufacturingSteel surfaceTexture segmentationSurface defects