Applications of Image Processing Techniques to Automated Detection of Cracks in Concrete Structures

• Main Authors: Ying-Liang Li, 李穎亮
• Other Authors: Tung-Ching Su
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/63342036343732574355

碩士 === 國立金門大學 === 土木與工程管理學系碩士班 === 100 === Most important civil infrastructures are made of concrete, so accurate information by routine inspection is necessary for structure maintenance. Currently most infrastructure inspections are implemented by inspectors. However, sometimes manual inspection would be inefficient and unsafe while the inspections of skyscraper or substructure of bridge are executed. In last decade, image-based techniques were applied to crack detection and measurement for concrete structures, such as principal component analysis, co-occurrence matrix, wavelet analysis, and statistical texture. However, the present detection accuracy between 68.7 and 76.5% is unsatisfied for practice applications. This research proposes a morphology-based image processing technique to attempt to automatically detect cracks in concrete structures. The morphology-based image processing technique consisting of weighted median filter, image opening operation, and image segmentation was used to transform the grey images of concrete structure into the binary images. To segment the image regions of complete crack from a noisy environment, two critical morphological features including area and eccentricity were measured for each segmented image region. Then, a sensitivity analysis based on the measured morphological features was applied to determine the appropriate criteria for crack detection. In this thesis, 100 images of the concrete roads and 50 ones of the concrete buildings were acquired to be the training and testing samples, respectively. The experimental result indicates that the optimal training and testing accuracies are 90% and 76%, respectively.