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Therefore, a twofold classification of steel members according to their ductility and overstrength is the most appropriate approach for seismic design applications.
Currently, modern international design codes are based on the classification of steel sections for both plastic and seismic designs of structures, providing misleading emphasis mainly on local buckling as the primary strain-weakening effect.
Various methods/techniques used for defect detection and classification of steel surfaces are listed in the literature.
Considering its importance, this paper attempts to make the first formal review of state-of-art of vision-based defect detection and classification of steel surfaces as they are produced from steel mills.
Therefore, in this paper, attempt has been made to consolidate the published literature from academia, steel industry and manufacturers on the topic of automatic defect detection and classification of steel surfaces.
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It is also suggested that significant improvement of EN 1993 predictions can be achieved revising the classification of high-strength steel CHS sections.
Classification of defects in steel surfaces is important for identifying and subsequently correcting causative factors.
However, the Gabor feature is the most suitable for texture representation and discrimination of steel defect classification.
Results suggest that the proposed method is a potential non-destructive technique for the characterization and classification of heat-resistant austenitic steel tubes with different aging states.
However, the authors could not locate any review of research work done in the field of steel surface defect detection and classification.
Defect detection and classification in steel surfaces broadly follow three steps: Localisation of candidate defects/regions of interest (RoIs) by means of segmentation, extraction of features from RoIs and finally, classification into defects and pseudo defects.
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