Crack Detection of Concrete Images Using Dilatation and Crack Detection Algorithms

Abstract

Crack detection in structures is an important and time-consuming element of monitoring the health of structures and ensuring structural safety. The traditional visual inspection of structures can be unsafe and may produce inconsistent results. Thus, there is a need for a method to easily and accurately identify and analyze cracks. In this study, algorithms for automatically detecting the size and location of cracks in concrete images were developed. Cracks were automatically detected in a total of 10 steps. In steps 5 and 9, two user algorithms were added to increase crack detection accuracy, where 1000 crack images and 1000 non-crack images were used, respectively. In the crack image, 100% of the cracks were detected, but 95.3% of the results were very good, even if the results that were not bad in terms of quality were excluded. In addition, the accuracy of detecting non-crack images was also very good (96.9%). Thus, it is expected that the crack detection algorithm presented in this study will be able to detect the location and size of cracks in concrete. Moreover, these algorithms will help in observing the soundness of structures and ensuring their safety.

Keywords

• image processing
• crack detection
• concrete cracks
• user algorithms
• dilatation