ABSTRACT
Digital image processing techniques and advancements help in performing the process of defect detection in hard reflective metal surfaces. Automatic inspection systems have the potential significance to improve quality and increase the production rate. The longitudinal cracks are common defects of continuous casting and may lead to serious quality issues. Image capturing and recognition of hot casting is an effective way detect cracks, and recognition of cracks is essential because the surface of the hot casting is very complicated. To detect the surface cracks of the casting, the feature extraction method is used. The current research work uses multiclass support vector machine (multiclass SVM) technique one of the popular classifiers in image classification. The acquired image is preprocessed by removing the noise present in the image. K-means clustering is applied in segmenting the images based on the specified features, such as size, texture, contrast, etc. The proposed method shows enhanced results in classifying the defective region in the metal surface.
