20.500.12556/RUL-146416 Transfer and unsupervised learning an integrated approach to concrete crack image analysis 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 pretrained 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. clustering crack detection data mining image analysis transfer learning unsupervised learning gručenje odkrivanje razpok podatkovno rudarjenje analiza slik učenje s prenosom znanja nenadzorovano učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2023-05-31 12:59:47 2023-05-31 12:59:51 2024-02-15 03:39:34 0000-00-00 00:00:00 2023 0 0 14 str. iss. 4, art. 3653 Vol. 15 2023 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 004:69 2071-1050 10.3390/su15043653 142062083 sustainability-15-03653.pdf sustainability-15-03653.pdf 1 56EC78EE0950D4F4D437ADDE4A022B26 e80d1f12e0392c2eacd4410f6e56e6b87ac8c80a9ef08ad4521224cafc42c512 74494f02-ffa2-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=169607 https://www.mdpi.com/2071-1050/15/4/3653 0 f6a9abe3-ffa1-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=169606 Fakulteta za gradbeništvo in geodezijo 0 0 0