Thickness Classifier on Steel in Heavy Melting Scrap by Deep-learning-based Image Analysis

Abstract

Avoiding the contamination of tramp elements in steel requires the non-ferrous materials mixed in steel scrap to be identified. For this to be possible, the types of recovered steel scrap used in the finished product must be known. Since the thickness and diameter of steel are important sources of information for identifying the steel type, in this study, the aim is to employ an image analysis to detect the thickness or diameter of steel without taking measurements. A deep-learning-based image analysis technique based on a pyramid scene parsing network was used for semantic segmentation. It was found that the thickness or diameter of steel in heavy steel scrap could be effectively classified even in cases where the thickness or diameter of the cross-section of steel could not be observed. In the developed model, the best F-score was around 0.5 for three classes of thickness or diameter: less than 3 mm, 3 to 6 mm, and 6 mm or more. According to our results, the F-score for the class of less than 3 mm class was more than 0.9. The results suggest that the developed model relies mainly on the features of deformation. While the model does not require the cross-section of steel to predict the thickness, it does refer to the scale of images. This study reveals both the potential of image analysis techniques in developing a network model for steel scrap and the challenges associated with the procedures for image acquisition and annotation.