Enhanced Performance for Support Vector Machines as Multiclass Classifiers in Steel Surface Defect Detection
Steel surface defect detection is essentially one of
pattern recognition problems. Support Vector Machines (SVMs) are
known as one of the most proper classifiers in this application. In this
paper, we introduce a more accurate classification method by using
SVMs as our final classifier of the inspection system. In this scheme,
multiclass classification task is performed based on the "one-againstone"
method and different kernels are utilized for each pair of the
classes in multiclass classification of the different defects.
In the proposed system, a decision tree is employed in the first
stage for two-class classification of the steel surfaces to "defect" and
"non-defect", in order to decrease the time complexity. Based on
the experimental results, generated from over one thousand images,
the proposed multiclass classification scheme is more accurate than
the conventional methods and the overall system yields a sufficient
performance which can meet the requirements in steel manufacturing.