A Robust Automated Vision Based Filamentous Steel Strip Crack Detection System Based on Neuron Model Segmentation
Asha Bharathi S1, Ravi Kumar M.S2

1Asha Bharathi S, M. Tech. degree in Digital Electronics and Advanced Communication from National institute of technology, Suratkal, India.
2Ravi Kumar M.S, Professor and Head of the Department of Electronics and Communication Engineering at K.V.G. College of Engineering, Sullia, India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 2940-2952 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8368038620/2020©BEIESP | DOI: 10.35940/ijrte.F8368.038620

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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.
Keywords: Automatic Steel Strip Crack Detection, Neuron-Model Segmentation, Active Contour Propagation, Region Growing, Level Set Concept.
Scope of the Article: Cyber Physical Systems (CPS).