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 real-time 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.
['Mohammadreza Yazdchi', 'Mehran Yazdi', 'Arash Golibagh Mahyari']
Steel Surface Defect Detection Using Texture Segmentation Based on Multifractal Dimension