Vol. 20 No. 1 (2023): Journal of Non Destructive Testing and Evaluation (JNDE), March 2023
Research Papers

Infrared Machine Vision for detection and characterization of defects present in a mild steel material

Geetika Dua
Thapar Institute of Engineering and Technology
Vanita Arora
Indian Institute of Information Technology Una, Vill. Saloh, Teh. Haroli, Distt. Una Himachal Pradesh, India-177209

Published 12-03-2023


  • Machine Vision,
  • Non-destructive testing,
  • Scale-invariant feature transform,
  • Watershed transform

How to Cite

Mahajan, P. Y., Bajwa, A. ., Keshav, Bansal, N. ., Dua, G., Kaur, A., & Arora, V. (2023). Infrared Machine Vision for detection and characterization of defects present in a mild steel material . Journal of Non-Destructive Testing and Evaluation (JNDE), 20(1), 38–43. Retrieved from https://jnde.isnt.in/index.php/JNDE/article/view/10


Significant advancements in machine vision and thermal imaging have provided an advantage in non-destructive testing and evaluating different materials. This work presents an approach to combining thermal wave imaging with machine vision for a fast, accurate way to detect and characterize the sub-surface defects and their features present in a solid material. Machine vision-based defect detection approaches attract the research community due to their reliable performance in employing the stimulated thermal response in active thermal wave imaging. A thermal source stimulates the material surface while the infrared camera captures its thermal response, extracts features from the thermal pattern, and feeds them into a machine vision-based algorithm for characterization. This proposed method improves the detectability reliability regarding qualified characteristics of defects.


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