# Coal and Gangue Recognition Method Based on Local Texture Classification Network for Robot Picking
## 1 Introduction
While for one-step or direct methods, classifiers are trained directly to recognize images instead of manually-extracted parameters. In one-step method, features are usually learned automatically from the raw input compared to two-step method. Figure 1: General framework of automated vision-based coal and gangue separation system. An example of a physical separation device is a robot with picking end-effector, e.g., gripers. [PERSON]'s work [9] was a representation of two-step methods. It extracted eight characteristic parameters from the Gray Level Co-occurrence Matrix (GLCM) of the original full-scale images and trained an SVM model or a B.P. neural network as a prediction boundary. [PERSON] [10] also analyzed the gray information feature parameters like gray mean value, gray histogram, and slope value. Several solutions are proposed to strengthen the performance of GLCM, like partial grayscale compression extended coexistence matrix [11]. Gray information was extracted to recognize coal and gangue images using partial grayscale compression with extended coexistence. However, their research strongly relies on coal's and gangue's distinct light reflection feature, which can be easily affected by the environment. [PERSON] [12] noticed that the different patterns in the shape of coal and gangue edges could be an indicator of category. The study applied Multifractal Detrending Fluctuation Analysis (MDFA [13]) on the images and trained the classifier for recognition. In [PERSON]'s paper [14], the case of coal and gangue covered by dust stains on the surface is discussed. A total of twelve colors and texture features are used for a support vector machine (SVM) classifier. [PERSON] [15] extracted color and texture features, respectively, for the latter classification. These works are in the two-step structure, which performs classification with trained models like SVM or neural networks on features extracted from original images. One-step methods are direct. [PERSON] trained a VGG16 network directly on a one-hundred-sized dataset for classification [16]. The transfer learning method was also applied to fir the model with a limited data volume and obtain an accuracy rate of 82.5%. [PERSON] [17] implemented an improved convolutional neural network based on AlexNet [18] to solve the discrimination problem and discussed the detection and location of target objects against the background. [PERSON] [19] carried out a CNN model to classify thermal images of coal and gangue. By studying the previous works and examining diverse datasets from different environments and devices, we noticed these methods would be prone to fail when applied to datasets generated in different conditions. Inspired by the texture recognition on clothes [20], we assumed that texture is an important factor in classification. Therefore, this paper proposes a Local Texture Classification Network (LTC-Net) method to improve the classification performance in different working conditions. The rest of this paper is as follows. In Section 2, the recognition problem of coal and gangue classification is presented. In Section 3, the limitation of existing methods is discussed under certain circumstances and proposes a coal and gangue recognition method based on LTC-Net. In Section 4, a group of experiments on various datasets with baseline methods and our method as well as the results and parameters' effect are discussed. Finally, we draw the conclusion of the paper.