Efficient and accurate segmentation of adhesive lamellae in tri-modal microstructure of titanium alloy based on OpenCV

• Published in: Materials characterization 2024-02, Vol.208, p.113606, Article 113606
• Main Authors: Wu, Huili, Xu, Liangliang, Xiao, Ning
• Format: Article
• Language: English
• Subjects: Adaptive detection threshold; Adhesive lamellae segmentation; Genetic algorithm; Harris corner detection; Tri-modal microstructure
• ISSN: 1044-5803
• DOI: 10.1016/j.matchar.2023.113606

The adhesive characteristics of the tri-modal microstructure in titanium alloys, particularly the adhesion of the lamellar phase that constitutes over 50% of the microstructure, significantly hinder its quantitative characterization, further impeding the establishment of the quantitative relationships between the microstructure and mechanical properties. Addressing this challenge, this study utilized the OpenCV computer vision library to accomplish efficient and precise automated segmentation of the adhesive lamellae in tri-modal microstructure by enhancing the existing segmentation algorithm based on concave point matching. Specifically, by taking the number of the adhesive target after segmentation and Harris corner detection threshold as the optimization goal and variable respectively, and employing the genetic algorithm, the threshold, accounting for the segmentation effect, was adaptively determined to accurately identify the corner points of the adhesive lamellae. And then quick determination of the concave points within these corner points was achieved by substituting conventional pixel circle with equidistant pixels distributed along the circumference. Upon identifying the concave points, classification of the points was performed based on their spacing and location, and corresponding matching criteria were established and applied. Eventually, by introducing segmentation lines between the matched points, adhesive lamellae were segmented with an accuracy exceeding 85%, which will lay a robust foundation for the rapid and automated quantitative characterization of the tri-modal microstructure in titanium alloys. •The corner detection threshold accounting for segmentation effectiveness is adaptively determined.•A rapid identification method for concave points is put forward.•An improved segmentation algorithm based on classified concave matching is proposed.