An RGB-D-Based Thickness Feature Descriptor and Its Application on Scrap Steel Grading

Scrap steel grading is a vital stage of steel production. Thickness is an important indicator in determining the grade of scrap steel. Existing methods based on 2-D vision cannot accurately estimate the thickness of scrap steel due to the lack of 3-D information. Consequently, estimation results from 2-D methods cannot satisfy the requirements of the steel industry. To solve this problem, we propose a 3-D vision-based scrap steel grading system. We introduce a novel thickness feature descriptor that can perform edge detection on organized point clouds. It can detect the pairs of points that satisfy the thickness feature from edge points. Once the detection of all thickness features is completed, invalid thickness features are filtered and correct thickness features are merged based on the contextual information. Finally, a thickness feature histogram (TFH) is calculated, and TFH is used for grading scrap steel. Our proposed method was integrated into a scrap steel grading system, which has been used in an actual production environment. Comprehensive experiments have been conducted to validate the proposed method. Moreover, a specialized scrap steel image dataset was created to train deep-learning-based methods in comparison experiments. Experimental results show that our method can extract thickness features and estimate the distribution of thickness of scrap steel effectively. The grading system has been successfully used in actual task scenarios and satisfies the industrial grading requirements. The scrap steel dataset is publicly available at https://github.com/zichengzichengzi/Scrap Steel Image Dataset.