Building Surface Crack Detection Using Deep Learning Technology

Abstract

Cracks in building facades are inevitable due to the age of the building. Cracks found in the building facade may be further exacerbated if not corrected immediately. Considering the extensive size of some buildings, there is definitely a need to automate the inspection routine to facilitate the inspection process. The incorporation of deep learning technology for the classification of images has proven to be an effective method in many past civil infrastructures like pavements and bridges. There is, however, limited research in the built environment sector. In order to align with the Smart Nation goals of the country, the use of Smart technologies is necessary in the building and construction industry. The focus of the study is to identify the effectiveness of deep learning technology for image classification. Deep learning technology, such as Convolutional Neural Networks (CNN), requires a large amount of data in order to obtain good performance. It is, however, difficult to collect the images manually. This study will cover the transfer learning approach, where image classification can be carried out even with limited data. Using the CNN method achieved an accuracy level of about 89%, while using the transfer learning model achieved an accuracy of 94%. Based on this, it can be concluded that the transfer learning method achieves better performance as compared to the CNN method with the same amount of data input.

An Integrated Framework for Image Acquisition, Processing, and Analysis Procedures for Automated Damage Evaluation of Concrete Surfaces

“…Nevertheless, there is a shortage of datasets containing the required images to train machine learning models for quantifying the extent of cracking (i.e., minimal annotated datasets) [16,17]. Moreover, most of the literature is focused on the crack detection task [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] where images are To perform a quantitative analysis of the damage extent to monitor volumetric expansion over time, researchers have introduced a surface crack mapping technique known as the cracking index (CI). This non-destructive quantitative tool assesses the degree of damage, estimating concrete expansion by measuring the widths of cracks observed on the ASR-affected concrete surface [6,7].…”

“…Nevertheless, there is a shortage of datasets containing the required images to train machine learning models for quantifying the extent of cracking (i.e., minimal annotated datasets) [16,17]. Moreover, most of the literature is focused on the crack detection task [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] where images are taken near the surface and without quantification of the damage. In a condition assessment, a quantitative value is required to inform the decision regarding the next steps and to monitor the increase in the damage over time to capture the rate of the damage.…”

Methods for detecting and counting nodes in images of crack networks

“…This paper proposes segmentation methods using computer vision algorithms for images of a network of conductors obtained on microcrack patterns. A number of authors also pay great attention to the problem of crack detection and segmentation [4,5]. These studies are mainly related to the construction industry, in particular the detection of cracks on the walls of buildings or other structures in order to predict their possible further destruction, however, they provide a good starting point for creating a technique for segmenting images of microcracks.…”

Digital Twin Research on Masonry–Timber Architectural Heritage Pathology Cracks Using 3D Laser Scanning and Deep Learning Model

“…In recent years, efforts by Chen, Y., Wang, N., and Wang, Z. [5][6][7], among other scholars, have resulted in the development of a single-crack recognition model based on convolutional neural networks for building surfaces, marking the application of deep learning in architectural research. However, the model's limited range of recognition capabilities restricts it to identifying concrete structures and single cracks on building surfaces, with image acquisition relying solely on manual observation and camera capture.…”