HCCD: Haar-Based Cascade Classifier for Crack Detection on a Propeller Blade

  • R. SaveethEmail author
  • S. Uma Maheswari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1045)


Crack detection in aircraft components is an important assessment because even a small unnoticed crack tends to critical crack length. Aviation demands reliability, and therefore, periodical inspection of cracks in aircraft parts like engine turbine blade, aircraft skin, rivets, wing spar, bulk fuselage, and airwings has to be detected in a fixed interval, but it requires human effort and expert’s knowledge. Features are extracted using extended Haar-like features and it has been given as input to cascade classifier to classify cracks and non-cracks images of a propeller blade. The supervised learning algorithm is developed and trained by a set of positive and negative images. The experimental results validate the test images by the cascading classifier to locate cracks.


Propeller blade Crack detection Cascading classifier Haar-like features Supervised learning 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringCoimbatore Institute of TechnologyCoimbatoreIndia
  2. 2.Department of Electronics and Communication EngineeringCoimbatore Institute of TechnologyCoimbatoreIndia

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