Abstract: The different mix ratios of concrete determine the performance of the material. The research on the classification of concrete images with various mix ratios and particle sizes is conducive to the efficient recycling of industrial waste concrete. In order to improve the classification effect, a new feature extraction module—image texture feature aided deep learning feature (ITFA-DLF) was proposed. This module employed convolution on the R, G, and B channels reconstructed from image separation and reconstruction. Convolutional neural network (CNN) extracted the color features of the three-channel image, utilized the multi-block local binary pattern (MB-LBP) to extract the texture features of the three-channel image, and merged the two features and input them into the support vector machine (SVM) optimized by the grid search (GS) algorithm for classification. Experiments with concrete images were adopted to compare various classification methods. It is concluded that the model proposed can produce the best effect. The recognition rate of nine types of images has reached more than 92%, and the classification time was shortened while ensuring the classification accuracy, and the classification efficiency of the concrete image was improved, which verified the effectiveness of the proposed method. 

Key words:  , concrete image, convolutional neural network, multi-block local binary pattern, feature fusion, support vector machine