Automatic Coal and Gangue Segmentation Using U-Net Based Fully Convolutional Networks

Abstract

Sorting gangue from raw coal is an essential concern in coal mining engineering. Prior to separation, the location and shape of the gangue should be extracted from the raw coal image. Several approaches regarding automatic detection of gangue have been proposed to date; however, none of them is satisfying. Therefore, this paper aims to conduct gangue segmentation using a U-shape fully convolutional neural network (U-Net). The proposed network is trained to segment gangue from raw coal images collected under complex environmental conditions. The probability map outputted by the network was used to obtain the location and shape information of gangue. The proposed solution was trained on a dataset consisting of 54 shortwave infrared (SWIR) raw coal images collected from Datong Coalfield. The performance of the network was tested with six never seen images, achieving an average area under the receiver operating characteristics (AUROC) value of 0.96. The resulting intersection over union (IoU) was on average equal to 0.86. The results show the potential of using deep learning methods to perform gangue segmentation under various conditions.

Automatic Asbestos Control Using Deep Learning Based Computer Vision System

“…The analysis and evaluation of performance and productivity of valuable rock resources mining, in particular, asbestos mining in an open pit, is one of the priority tasks in the mining industry [1][2][3][4][5][6]. As a rule, the estimation of performance (productivity) in an open pit is carried out either visually or in the stationary laboratory.…”

“…Visual control is the immediate one that is carried out by specialists of the geological service almost online but with comparatively low accuracy. The stationary laboratory control provides high accuracy but it is time-consuming [1]. The additional investigation of the other specificity of the laboratory-based asbestos content estimation is described in [7].…”

“…The analysis of the known engineering solutions in the mining industry has shown that contemporary computer vision systems allow the automation of such mining processes as coal and rocks classification on the conveyor [2]; the rocks and coal sorting by size [1] and types [3]; the ore size determination [4]; the fossil identification and classification in ore [8]; the asbestos fiber size estimation under microscopy [9]; and other tasks.…”

“…In most denoted and other cases, the computer vision systems for automating the mining industry are based on image processing using artificial neural networks, namely convolution neural networks (CNN) [1][2][3][4]8,9]. Moreover, in most cases, the image processing task is the object detection one [1,2,4].…”

“…The U-Net is the CNN-based architecture that was initially proposed for the image analysis in medical applications where it shows high accuracy, in particular, for small objects [12,13]. However, nowadays the U-Net-based approach is also popular in many of non-medical applications, in particular, the mining industry [1][2][3][4]8]. For instance, in [1], the U-Net was applied for automatization of sorting gangue from the raw coal with the training only with 60 images (54 for training and 6 for the test).…”