Efficient crack detection and quantification in concrete structures using IoT

Now-a-days public transit in our country has a significant usage of bridges and tunnels. Maintaining the safety of such structures becomes the need of the hour. Cracking can invite sudden failures of concrete structures. Within recent years, there has been an increase in the use of image processing techniques as Non-Destructive Testing (NDT) method to detect defects and anomalies in such structures. Hence, this work presents an efficient image processing model for identifying and quantifying the cracks in common structures using two algorithms. The first algorithm uses pattern classification method to classify the identified specimens as crack or not. This pattern classification algorithm is tested and evaluated with the test specimens of structures having induced cracks. The second algorithm called morphological processing uses an efficient thresholding strategy to improve the detection accuracy of the cracks. Here the aim is to detect even the faintest of cracks and magnify them. Thus, any one of the algorithms can be adopted depending on the application. The developed algorithms have been tested in real field at the final stage and achieved a detection accuracy of nearly 96.37%and all the obtained images have been stored digitally on a web server.