Morphology-Based Crack Detection for Steel Slabs

Abstract

Abstract-Continuous casting is a highly efficient process used to produce most of the world steel production tonnage, but can cause cracks in the semi-finished steel product output. These cracks may cause problems further down the production chain, and detecting them early in the process would avoid unnecessary and costly processing of the defective goods. In order for a crack detection system to be accepted in industry, however, false detection of cracks in non-defective goods must be avoided. This is further complicated by the presence of scales; a brittle, often cracked, top layer originating from the casting process.We present an approach for an automated on-line crack detection system, based on 3D profile data of steel slab surfaces, utilizing morphological image processing and statistical classification by logistic regression. The initial segmentation successfully extracts 80% of the crack length present in the data, while discarding most potential pseudo-defects (non-defect surface features similar to defects). The subsequent statistical classification individually has a crack detection accuracy of over 80% (with respect to total segmented crack length), while discarding all remaining manually identified pseudo-defects. Taking more ambiguous regions into account gives a worst-case false classification of 131 mm within the 30 600 mm long sequence of 150 mm wide regions used as validation data. The combined system successfully identifies over 70% of the manually identified (unambiguous) crack length, while missing only a few crack regions containing short crack segments.The results provide proof-of-concept for a fully automated crack detection system based on the presented method.

Topographic Surface Inspection System for Slabs

“…Therefore particular notice needs to be taken to the presence of scale, which constitute a brittle, often cracked, top layer, formed from oxidization in the manufacturing process. This scale layer is unavoidable during casting, and cracks therein are from a top view perspective similar to cracks in the steel and therefore risk causing false positives in the detection result [8]. But the benefit of gray-scale intensity images in the presence of scale is limited: Variations in lightning conditions may lead to potential pseudo-defects.…”

“…Range-imaging sensors collect large amounts of three-dimensional (3-D) coordinate data from visible surfaces in a scene and can be used in a wide variety of automation applications, including object shape acquisition, bin picking, robotic assembly, inspection, gaging, mobile robot navigation, automated cartography, and medical diagnosis [9]. In [8] a strategy for morphology-based crack detection for steel slabs based on 3D surface profile data collected by laser triangulation is presented. An advanced technology for hot slab surface inspection has been developed and installed in ACERALIA (Spain) [10].…”

Sub-Millimeter Crack Detection in Casted Steel Using Color Photometric Stereo

“…There exists many different NDT methods for defect detection in steel, such as thermocouples [1], eddy currents [2], ultra sound [3], conoscopic holography [4], and laser triangulation [5]. In this work however, we focus on the detection of small corner cracks which are in general thinner than 1 mm.…”

Application of Shearlet transform to classification of surface defects for metals

“…For example, an algorithm combined with discrete wavelet transform and morphological analysis was developed to detect corner cracks of steel billets from oxide scales [1]; Gabor filters were used to detect thin and corner cracks in raw steel block by minimizing the cost function of energy separation criteria of defect and defect-free regions [2]; defects of structural steel plates were detected using Discrete Fourier Transform Spectral Energy and Artificial Neural Networks [3]; an approach based on 3D profile data of steel slab surfaces was developed for an automated on-line crack detection system, and morphological image processing and logistic regression based statistical classification were integrated in the system [4]; a framework with multiple views was applied to detecting flaws in aluminum castings, and information gathered from multiple views of the scene was combined for the flaw detection [5]. Although the above methods achieved high detection rates of some defects, they were restricted to some specific products or defects, and classification rates of common defects were generally limited.…”