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.
- Image processing
- small datasets
- transfer learning
ASJC Scopus subject areas
- Civil and Structural Engineering
- Building and Construction
- Computer Science Applications