Transfer and unsupervised learning : an integrated approach to concrete crack image analysis

Gradišar, Luka (avtor), Dolenc, Matevž (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: clustering, crack detection, data mining, image analysis, transfer learning, unsupervised learning
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FGG - Fakulteta za gradbeništvo in geodezijo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2023
• Št. strani: 14 str.
• Številčenje: Vol. 15, iss. 4, art. 3653
• PID: 20.500.12556/RUL-146416
• UDK: 004:69
• ISSN pri članku: 2071-1050
• DOI: 10.3390/su15043653
• COBISS.SI-ID: 142062083
• Datum objave v RUL: 31.05.2023

Gradivo je del revije

• Naslov: Sustainability
• Založnik: MDPI
• ISSN: 2071-1050
• COBISS.SI-ID: 5324897

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: gručenje, odkrivanje razpok, podatkovno rudarjenje, analiza slik, učenje s prenosom znanja, nenadzorovano učenje

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Program financ.: Young researchers