Steel Surface Defects Detection and Classification Using SIFT and Voting Strategy

This paper describes a framework for steel surface defects detection and classification. The authors use SIFT for defects regions detection and features extraction for the following SVM classification. This approach can generate many feature points for training the classifier from a few images. They also propose a voting strategy for the final decision that handles the problem of multiple outputs of a given input image with a specific defect type. In addition, this approach improves the classification performance. Experimental results demonstrate the effectiveness of the proposed method on steel surface defects detection and classification.