Transfer and Unsupervised Learning: An Integrated Approach to Concrete Crack Image Analysis

Abstract

The detection of cracks in concrete structures is crucial for the assessment of their structural integrity and safety. To this end, detection with deep neural convolutional networks has been extensively researched in recent years. Despite their success, these methods are limited in classifying concrete as cracked or non-cracked and disregard other characteristics, such as the severity of the cracks. Furthermore, the classification process can be affected by various sources of interference and noise in the images. In this paper, an integrated methodology for analysing concrete crack images is proposed using transfer and unsupervised learning. The method extracts image features using pre-trained networks and groups them based on similarity using hierarchical clustering. Three pre-trained networks are used for this purpose, with Inception v3 performing the best. The clustering results show the ability to divide images into different clusters based on image characteristics. In this way, various clusters are identified, such as clusters containing images of obstruction, background debris, edges, surface roughness, as well as cracked and uncracked concrete. In addition, dimensionality reduction is used to further separate and visualise the data, making it easier to analyse clustering results and identify misclassified images. This revealed several mislabelled images in the dataset used in this study. Additionally, a correlation was found between the principal components and the severity of cracks and surface imperfections. The results of this study demonstrate the potential of unsupervised learning for analysing concrete crack image data to distinguish between noisy images and the severity of cracks, which can provide valuable information for building more accurate predictive models.

Development of Crack Detection and Crack Length Calculation Method using Image Processing

“…Image processing techniques are evolving daily [9,10,11], and in the field of architecture, many studies have been conducted on crack detection using image classification techniques, such as tunnel crack detection [12,13], bridge and road crack detection [14,15,16], and fatigue degradation detection of material deterioration [17,18]. Many of these studies used existing trained models to detect the presence or absence of cracks [19,20]. Crack detection has been studied for several years, and various detection methods have been proposed.…”