Efficient Crack Severity Level Classification Using Bilayer Detection for Building Structures

Abstract

Detection of cracks at the early stage is considered as very constructive since precautionary steps need to be taken to avoid the damage to the civil structures. Moreover, identifying and classifying the severity level of cracks is inevitable in order to find the stability of buildings. Hence, this paper proposes an efficient strategy to classify the cracks into fine, medium, and thick using a novel bilayer crack detection algorithm. The bilayer crack detection algorithm helps in extracting the requisite features from the crack for efficient classification. The proposed algorithm works well in the dark background and connects the discontinued cracks too. The first layer is used to detect cracks under texture variations and manufacturing defects, through segmented adaptive thresholding and morphological operations. The residual noise present in the output of the first layer is removed in the second layer of crack detection. The second layer includes the double scan and the noise reduction algorithms and is used to join the missed crack parts. As a result, a segmented crack is formed. Further classification is done using an ensemble classifier with bagging, and decision tree techniques by extracting the geometrical features and the weaker crack criterion from the segmented part. The results of the proposed technique are compared with the existing techniques for different datasets and have obtained a rise in True Positive Rate (TPR), accuracy and precision value. The proposed technique is also implemented in Raspberry Pi for further real-time evaluation.