Ontology based Approach using a Systemic Knowledge Model for Surface Defect Classification

An understanding of the image starts with sensing pertinent information, and subsequently recognize domain objects, based on a prior conceptualization. Thus, suitable modeling of the image content is essential to make use of the dependency between patterns in a particular domain. Through a computer interpretable model that results in a knowledge-based model, we can optimize the leverage of knowledge in image interpretation of a certain domain. In this paper, we focus on a systemic knowledge modeling intended for surface defect classification. Accordingly, we have exploited the image spatial information for building surface defect domain ontology. A set of statistical texture features has been extracted. A systemic approach of conceptualisation has been proposed, based on a decision tree classification, looking at filling the gap between low and medium level knowledge on the one hand and high level knowledge, which is defect detect categories on the other hand. Accordingly, the proposed ontology has been modeled with OWL and SWRL for reasoning and rule inference. The information, extracted from the grayscale image and its significance for deducing the surface flaws, is formalized to establish surface defect ontology. Validation of the proposed approach has been done on an industrial radio-graphs dataset NEU-DET. Compared to the state-of-the-art, our method yields on the same dataset a challenging performance of 85.87% in term of mean average precision (mAP).