Open Access

ARTICLE

Research on Defect Detection of Wind Turbine Blades Based on Morphology and Improved Otsu Algorithm Using Infrared Images

by Shuang Kang¹, Yinchao He^1,2, Wenwen Li^1,*, Sen Liu²

1 School of Mechanical and Control Engineering, Baicheng Normal University, Baicheng, 137000, China
2 School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132022, China

* Corresponding Author: Wenwen Li. Email:

Computers, Materials & Continua 2024, 81(1), 933-949. https://doi.org/10.32604/cmc.2024.056614

Received 26 July 2024; Accepted 26 August 2024; Issue published 15 October 2024

Abstract

To address the issues of low accuracy and high false positive rate in traditional Otsu algorithm for defect detection on infrared images of wind turbine blades (WTB), this paper proposes a technique that combines morphological image enhancement with an improved Otsu algorithm. First, mathematical morphology’s differential multi-scale white and black top-hat operations are applied to enhance the image. The algorithm employs entropy as the objective function to guide the iteration process of image enhancement, selecting appropriate structural element scales to execute differential multi-scale white and black top-hat transformations, effectively enhancing the detail features of defect regions and improving the contrast between defects and background. Afterwards, grayscale inversion is performed on the enhanced infrared defect image to better adapt to the improved Otsu algorithm. Finally, by introducing a parameter K to adjust the calculation of inter-class variance in the Otsu method, the weight of the target pixels is increased. Combined with the adaptive iterative threshold algorithm, the threshold selection process is further fine-tuned. Experimental results show that compared to traditional Otsu algorithms and other improvements, the proposed method has significant advantages in terms of defect detection accuracy and reducing false positive rates. The average defect detection rate approaches 1, and the average Hausdorff distance decreases to 0.825, indicating strong robustness and accuracy of the method.

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Keywords

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