ITcon Vol. 27, pg. 951-971, http://www.itcon.org/2022/46

Recognition of the condition of construction materials using small datasets and handcrafted features

DOI:10.36680/j.itcon.2022.046
submitted:April 2022
revised:September 2022
published:November 2022
editor(s):Amor R
authors:Eyob Mengiste
New York University Abu Dhabi, United Arab Emirates, and Technical University of Berlin, Germany
Email: eyob.mengiste@nyu.edu

Borja Garcia de Soto
New York University Abu Dhabi, United Arab Emirates
Email: garcia.de.soto@nyu.edu

Timo Hartmann
Technical University of Berlin, Germany
Email: timo.hartmann@tu-berlin.de
summary:We propose using handcrafted features extracted from small datasets to classify the conditions of the construction materials. We hypothesize that features such as the color, roughness, and reflectance of a material surface can be used to identify details of the material. To test the hypothesis, we have developed a pre-trained model to classify material conditions based on reflectance, roughness and color features extracted from image data collected in a controlled (lab) environment. The knowledge learned in the pre-trained model is finally transferred to classify material conditions from a construction site (i.e., an uncontrolled environment). To demonstrate the proposed method, 80 data points were produced from the images collected under a controlled environment and used to develop a pre-trained model. The pre-trained model was re-trained to adapt to the real construction environment using 33 new data points generated through a separate process using images collected from a construction site. The pre-trained model achieved 93%; after retraining the model with the data from the actual site, the accuracy had a small decrease as expected, but still was promising with an 83% accuracy.
keywords:image processing, transfer learning, roughness, reflectance, color, CIELab, small datasets
full text: (PDF file, 1.381 MB)
citation:Mengiste E, de Soto B G, Hartmann T (2022). Recognition of the condition of construction materials using small datasets and handcrafted features, ITcon Vol. 27, pg. 951-971, https://doi.org/10.36680/j.itcon.2022.046
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