A Robust Automated Vision Based Filamentous Steel Strip Crack Detection System Based on Neuron Model Segmentation

Abstract

Crack detection has always been a dominant requirement for steel industries to ensure quality production and seamless infrastructure maintenance. However, application complexities and defect morphological differences make existing approaches confined. Steel-strip surface often undergoes scratch, crack and fatigue conditions during production. Manual crack detection schemes are no longer effective in current day complex environment. Amongst major steel strip crack detection approaches vision based techniques have found potential; Filamentous crack which is caused due to fatigue or strain is fine-grained and thin and hence highly difficult to be detected by classical morphology and static threshold based schemes. In the present work steel strip surface (filamentous) crack detection system has been developed which employs Varying-Morphological Segmentation (VMS) also called Neuron-Model Segmentation (NMS) in conjunction with local directive filtering and active contour propagation. The proposed method can be stated as an augmented variational framework that employs multi-directional filters for local crack-region identification followed by automated multi-directional region growing and iterative contour evolution which performs level set energy minimization to achieve accurate crack detection even under topological non-linearity and varying illumination conditions Simulation results with standard benchmark data has confirmed that the proposed method exhibits satisfactory performance for steel strip surface cracks.