Sub-dataset Generation and Matching for Crack Detection on Brick Walls using Convolutional Neural Networks

Crack detection is an issue of significant interest in ensuring the safety of buildings. Conventionally, a maintenance engineer performs crack detection manually, which is laborious and time-consuming. Therefore, a systematic crack detection method is required. Among the existing methods, convolutional neural networks (CNNs) are more effective; however, they often fail in the case of brick walls. There are several types of bricks and some may appear to have cracks owing to their structure. Additionally, the joining points of bricks may appear as cracks. It is theorized that if sub-datasets are generated based on the image attributes, and a proper sub-dataset is selected by matching the test image with the sub-datasets, then the performance of the CNN can be improved. In this study, a method consisting of sub-dataset generation and matching is proposed to improve the crack detection in brick walls. CNN learning is conducted with each sub-dataset, and crack detection is performed using a proper learned CNN that is selected by matching the test images with the images in the sub-datasets. Four performance metrics, namely, precision, recall, Fmeasure, and accuracy, are used for performance evaluation. The numerical experiments show that the proposed method improves the crack detection in brick walls.