Classification and Identification of Steel Scrap with Deep Learning

Abstract

Following the industrial revolution, the rapidly increasing demand for steel has heightened the risk of depleting natural resources and intensified environmental impacts. In this context, the recycling of steel scrap holds significant importance for sustainable production. Particularly, the use of steel scrap in Electric Arc Furnaces (EAF) stands out as an environmentally friendly production method due to its high energy efficiency and low carbon footprint. However, the accurate and rapid classification of scrap to be used in EAFs is a critical requirement in terms of both process efficiency and the quality of the final product. In this thesis study, a system was developed to automatically classify scrap-laden trucks during their passage over the weighbridge. The developed system utilizes deep learning-based image processing techniques to automatically determine the scrap class and communicates the classification result to truck drivers via an LED display positioned on-site. The custom dataset consists of 11,787 labeled images representing 8 different scrap classes, with 1,407 images reserved for testing, collected from real field conditions. Among the models tested, ConvNeXt Large achieved the highest classification accuracy and directs trucks to the appropriate dumping area among 36 unloading points in two halls (A and B). With this system, the scrap acceptance process has been fully automated. Supervisor: Assoc. Prof. Dr. Levent Karacan.