TY  - EJOU
AU  - Kang, Shuang 
AU  - He, Yinchao 
AU  - Li, Wenwen 
AU  - Liu, Sen 

TI  - Research on Defect Detection of Wind Turbine Blades Based on Morphology and Improved Otsu Algorithm Using Infrared Images
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 81
IS  - 1
SN  - 1546-2226

AB  - 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.
KW  - Morphological enhancement; improved Otsu algorithm; infrared image; grayscale inversion; adaptive iterative thresholding

DO  - 10.32604/cmc.2024.056614