A corner-clustering method for detection of slab management numbers sprayed on steel slabs

Abstract

To achieve manufacturing and logistics informatization management for steelworks, it is of crucial importance to automatically recognize the slab management numbers (SMNs) sprayed on the steel slabs. However, due to the poor quality of spraying and various interferences, SMN detection is a major challenge for subsequent recognition in the steel-slab product line. This paper proposes a corner-clustering method, which can extract the SMN from a changeable background precisely and promptly. In our method, the FAST algorithm is modified to extract the image corners by adaptively adjusting the local threshold of corner detecting with the change of image contrast. Then, the DBSCAN algorithm is implemented to group the corners into several clusters, which includes the SMN regions and interference regions. Finally, a classifier based on HOG features and SVM is applied to discriminate SMN and non-SMN regions. For experimental validation, the proposed method was implemented to a substantial amount of acquired images. A good performance has been achieved as the detection accuracy can reach as high as 98.96% for SMN on the steel slabs.