CUED Publications database

Parameter optimization for automated concrete detection in image data

Zhu, Z and Brilakis, I (2010) Parameter optimization for automated concrete detection in image data. Automation in Construction, 19. pp. 944-953. ISSN 0926-5805

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Several research studies have been recently initiated to investigate the use of construction site images for automated infrastructure inspection, progress monitoring, etc. In these studies, it is always necessary to extract material regions (concrete or steel) from the images. Existing methods made use of material's special color/texture ranges for material information retrieval, but they do not sufficiently discuss how to find these appropriate color/texture ranges. As a result, users have to define appropriate ones by themselves, which is difficult for those who do not have enough image processing background. This paper presents a novel method of identifying concrete material regions using machine learning techniques. Under the method, each construction site image is first divided into regions through image segmentation. Then, the visual features of each region are calculated and classified with a pre-trained classifier. The output value determines whether the region is composed of concrete or not. The method was implemented using C++ and tested over hundreds of construction site images. The results were compared with the manual classification ones to indicate the method's validity.

Item Type: Article
Uncontrolled Keywords: Artificial intelligence Concrete Identification Image Information techniques
Divisions: Div D > Construction Engineering
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:00
Last Modified: 10 Apr 2021 00:50
DOI: 10.1016/j.autcon.2010.06.008