Coal and Gangue Separating Robot System Based on Computer Vision

Abstract

In coal production, the raw coal contains a large amount of gangue, which affects the quality of coal and pollutes the environment. Separating coal and gangue can improve coal quality, save energy, and reduce consumption and make rational use of resources. The separated gangue can also be reused. Robots with computer vision technology have become current research hotspots due to simple equipment, are efficient, and create no pollution to the environment. However, the difficulty in identifying coal and gangue is that the difference between coal and gangue is small, and the background and prospects are similar. In addition, due to the irregular shape of gangue, real-time grasping requirements make robot control difficult. This paper presents a coal and gangue separating robot system based on computer vision, proposes a convolutional neural network to extract the classification and location information, and designs a robot multi-objective motion planning algorithm. Through simulation and experimental verification, the accuracy of coal gangue identification reaches 98% under the condition of ensuring real-time performance. The average separating rate reaches 75% on low-, medium-, and high-speed moving conveyor belts, which meets the needs of actual projects. This method has important guiding significance in detection and separation of objects in complex scenes.

Intelligent Gangue Sorting System Based on Dual-Energy X-ray and Improved YOLOv5 Algorithm

“…Not only could it achieve the precise identification of the gangue, but it also obtained information about the relevant locations of the gangue, which could be applied to the target detection task of the gangue. Sun et al [17] studied the classification and location of coal and gangue, proposed a dynamic object detection approach in view of CG-YOLO, and used robots to grab irregularly shaped gangue to form a complete separation system. Luo et al [18] improved the backbone and neck of YOLOv5, implemented a deep lightweight target inspection network, and ensured speed and precision in detecting multiple types of materials on belt conveyors.…”

A study on the influence of particle size on the identification accuracy of coal and gangue

“…Radiation-based image recognition technology is used to reflect raw coal on the conveyor belt with γ and X rays. γ and X-rays can be identified by different degrees of radiation absorption or attenuation in coal and gangue (Zhang and Liu 2018;Robben et al 2020), but this kind of equipment is radioactive (Sun et al 2021). CCD industrial cameras use no radiation and low cost, so they are widely used.…”

Adaptive image enhancement and particle size identification method based on coal and gangue

“…However, γ-ray is not widely used because of its strong radiation and high price [12]. The visible light-based machine vision technology [13] has no radiation, high efficiency, and low cost, and is widely applied to the field of coal and gangue identification. Based on machine vision technology, Li et al found that the four features of gray variance, skewness, texture contrast, and entropy have the strongest separability through the analysis of the grayscale and texture information of coal gangue [14].…”