# Coal and Gangue Recognition Method Based on Local Texture Classification Network for Robot Picking
## 1 Introduction  
Coal gangue, or gangue, is a kind of black or gray solid waste discharged in coal mining and coal washing. Compared to the widely-used fossil fuel, gangue provides much less energy supply and contains less value due to lower carbon content. It can also be a pollution source in the absence of proper treatments [1]. Thus, the separation of gangue and coal in raw coal production workshops is critical to improving coal quality, minimizing the storage and transportation cost, and protecting the environment [2]. Higher utilization rates of coal could also help slow down global warming by reducing total coal burning [3]. Conventional methods for coal and gangue separation are mainly manual or machine-based. A typical underground coal-mining production line transports mixed coals and gangues onto ground-level workshops with conveyor belts and feeds them into the separation process. The manual method requires workers on both sides of the belt, removing gangues manually. Since the dust and high temperature could render a harsh working environment, coal yards today incline to machine-based methods. A coal-washing machine, which separates coals and gangues automatically, is the most widely used one to free workers from intensive, repetitive, and arduous separation movements. It is essentially a centrifugal machine utilizing the different densities of coal and gangue. X-rays or gamma-ray transmission sensors are also adopted by workshops, among other machine-basedmethods [2]. Besides, there are also other separating methods based on coal's and gangue's different physical characteristics such as vibration tests [4]. However, the high mechanical quality requirement to ensure productivity leads to high purchase and maintenance costs of these machines [5]; moreover, soaring energy consumption and potential air pollution can also be a problem. With the rapid development of computer vision technology and computing capability growth, low-cost vision-based coal and gangue automated separation systems have emerged. Figure 1 depicts the general framework of an automated vision-based coal and gangue separation system. It can be divided into three core components as follows:  
* Vision unit: Normally, an industrial camera with illumination devices captures digital images at a fixed frequency in correspondence with the constant-speed conveyor belt. * Control unit: Normally, a micro-computer with a coal and gangue recognition model (e.g., classification algorithm) with an image from vision unit as input and a control command based on the recognition result as output. * Separation unit: A physical separation device (e.g., a robotic system with a griper or robotic hand [6; 7]) operating based on the output command of the control unit to remove recognized gangues. [PERSON]'s work put forward a coal and gangue separating robot system based on computer vision [8]. Figure 1 shows a simplified working flow: images are captured by the vision unit and fed into the control unit, which performs recognition on each image and gives a control command regarding the enabling status of the separation unit based on the recognition result. If an image is recognized as a gangue, the separation unit will execute the predefined mechanical operation once to remove the recognized gangue from the conveyor. With the distributed structure and relatively lower-cost devices, vision-based systems have an advantage in cost and maintenance over machine-based methods. However, it is also worth noting that vision-based systems' reliability and effectiveness in realistic industry depend significantly on the recognition model's accuracy. Promising vision-based methods about coal and gangue image recognition models arose over recent years. There are two types of methods: two-step and one-step. For two-step methods, predefined hyper characteristic parameters are first manually extracted from original images, and then classifiers are trained based on these parameters.