Recognition of Concrete and Gray Brick Based on Color and Texture Features

Identification and classification of construction waste are two important aspects of construction waste recycling. This study proposes a method based on image processing to identify gray brick and concrete, the most difficult types to identify that have the highest percentage of construction waste. By combining color features with texture features, machine learning algorithms are used for training and recognition. We paid great attention to the comparison of the performance of different color models, which includes Red, Green, Blue (RGB), Hue, Saturation, Value (HSV), and Lab. We found that gray histograms and color moments were suitable as color features of concrete and gray bricks. Meanwhile, the eigenvalues of the gray-level co-occurrence matrix (GLCM) were also discussed. Contrast, angular second moment, inverse different moment, and correlation in the five eigenvalues of GLCM were selected as texture features via experiments. We used three machine learning algorithms to train the extracted data. The results showed that the extreme learning machine had the lowest accuracy (96.25 %), whereas the support vector machine and back propagation algorithm had higher accuracy of 96.875 % and 98.125 %, respectively. The online testing had the accuracy of 95 %, indicating that the selected features are effective, and the accuracy can meet the engineering needs.