Automatic Detection of Subsurface Anomalies using Non-Linear Chirped Thermography
B. Suresh1, Sai kiran Jammula2, G V Subbarao3
1B. Suresh, Assistant Professor, Department of ECE, Koneru Lakshmaiah Educational Foundation, Vaddeswaram (Andhra Pradesh), India
2Sai Kiran Jammula, B. Tech Student, Department of ECE, Koneru Lakshmaiah Educational Foundation, Vaddeswaram (Andhra Pradesh), India
3G.V.Subbarao, Professor, Department of ECE, Koneru Lakshmaiah Educational Foundation, Vaddeswaram (Andhra Pradesh), India
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 1247-1249 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3859048619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Detection of subsurface anomalies facilitates easy assessment with assuring the industrial quality of materials. Infrared imaging has found to be promising non-intrusive approach to cater to it. But the expertise required for the thermographic analysis is time consuming and for the laborious detection task over the post-processed history leads to limitation of its applications. Any automatic defect detection procedures along with post-processing facilitates practitioner to get comprehensive information without manual intervention. This manuscript introduces a level set based image segmentation to locate anomalies in post-processed Frequency Modulated Thermal Wave Image (FMTWI).To ratify the applicability of the method proposed, the trail is done on the Teflon patches specimen.
Keyword: Infrared Non-Destructive Testing, QFMTWI, Pulse Compression (PC), Level-set.
Scope of the Article: Software and System Testing Methods