Multi-scale convolutional neural network for texture recognition.

Texture is of great significance for humans and robots to recognize the surface features of objects. In the field of texture recognition, methods based on spatial information have been widely applied. However, in the case of fine texture recognition, the methods only using spatial features for texture recognition may ignore the features of small texture and result in poor recognition accuracy. In this paper, a Multi-Scale Convolutional Neural Network (MS-CNN) is proposed to recognize millimetric fine textures. MS-CNN has three paths to extract features of different time scales from different numbers of continuous pressure images. The three paths have the same backbone network structure, but the number of convolution cores of the convolution layer in the backbone network of adjacent paths is doubled. After the convolution layer, we add SE-Net to automatically obtain the importance of each feature channel through learning, and then improve the useful features to further improve the accuracy. Finally, the output of all paths is averaged, and the classification vector is calculated through the final full connection layer. To validate MS-CNN, data sets containing 9 kinds of millimetric fine textures are obtained by flexible tactile sensors. The pressure image is transformed into one-dimensional vectors and these vectors are arranged into a sample in time order as the input of MS-CNN. In addition, the attention mechanism module is applied to MS-CNN to train the weight of each convolution channel and increase the proportion of useful features in the network. Ablation experiments prove that our modification is effective and our method achieves an ac-curacy of 81.83% for 9 fine textures. Compared with traditional recognition methods, our method achieves better recognition performance.