Separation Between Coal and Gangue Based on Infrared Radiation and Visual Extraction of the YCbCr Color Space

Abstract

Distinguishing between coal and gangue in the production lines of mining factories based on the thermal energy and infrared radiation emission of an object is feasible. In this paper, we use an infrared camera (IC) to distinguish between coal and gangue in the industrial mining field. Additionally, this system is considered to be a binary classification system that has two classes. We analyze the infrared images of coal and gangue; then extract the appropriate texture features from the infrared images in order to develop an accurate classification system by using support vector machine (SVM). The method applied in this work essentially depends on feature extraction of images. The statistical features based on gray level information (GLI), grey-level cooccurrence matrix (GLCM) and visual features are executed. Thus, we suggest preparation steps to obtain one select feature before importing the data into the SVM classifier, and this approach is adopted as the fundamental basis for our work. We exploit only one feature of the infrared image, namely, Cb, which is extracted from the YCbCr color space, and then compute the mean value of Cb after heating and capturing the photos for the coal and gangue samples. The proposed method achieves a high classification accuracy 97.83 % by using Gaussian-SVM.

Coal/Gangue Recognition Using Convolutional Neural Networks and Thermal Images

“…Thermal images was captured for the Coal/Gangue in certain conditions to increase the difference characters between the two types because of the variability of heat factor taking in account that the Coal/Gangue react by different degrees for the heating environment, a hypothesis has been proposed to say that putting the Coal/Gangue in hot environment and capture the thermal images of the surface will make the classification of the Coal/Gangue more efficient, this hypothesis has been discussed extensively in (SVM-YCbCr) [24], the Coal/Gangue samples have been collected from Bituminous coal, produced in Shanxi Province, western of China, they were put in thermal container until they reach 50 Celsius, after that 139 thermal images (70 coal, 69 gangue) have been captured using the thermal camera which generate thermal images of (.IS2) extension then using the Fluck SmartView 3.1 application the captured thermal images (.IS2) have been converted into PNG images with (680×480) pixels resolution as shown in Figure 1, in the experiment the dataset has been divided into three categorizes training (91), validation(28) and testing (20), Table 1 shows the data set divided between the three phases of the experiment. But number of images still not enough and inevitably will case over fitting problem, this problem has been addressed by many researchers in there works and to solve the scarcity of image resources an augmentation process performed in the small dataset in order to increase it with respect to generate different pixel values in the same position to make sure that a different image are generated beside the original image, Krizhevsky et al [26] in the Alexnet used the augmentation principles to increase the data set in there work, so in order to increase the dataset samples here the augmentation principle has been applied, first the images have been centered and cropped into (480 × 480) pixels resolution to be suitable for augmentation process, then three rotation processes with degrees (90,180,270) have been done and increased the data set from 139 into 556 after that a horizontal inverting has been done to create 1112 images divided in the three categories as explained in Table 2, Figure 4 shows the transformation done to an image and the new generated images and the differences between them.…”

“…This paper present a development for the work titled ''Separation between Coal and Gangue based on Infrared Radiation and Visual Extraction of the YCbCr Color Space'' by Eshaq et al [24] mentioned here as SVM-YCbCr, by developing a Convolutional Neural Network model for recognition rather than using SVM and feature extraction processes, in the matter of recognition accuracy CGR-CNN raise up the coal recognition accuracy to (100%) compared with (96.6%) in SVM-YCbCr, also achieve a near gangue recognition reached (97.5%) compared to (98.1%) which lead to a recognition accuracy (98.75%) for CGN-CNN compared to (97.83%) in SVM-YCbCr, also in the matter of reducing the execution time using CNN lead to reduce a lot of preprocessing steps which will reduce the latency during the operation time and increase the production efficiency, based on the experiment time(TABLE 7: THE TIME RESULTS OF ACQUISITION, READING, PROCESSING, TRAIN-ING AND PREDICTION OF INFRARED IMAGES) in [24] the acquisition time is for a samples preparation phase which take different amount of coal and gangue in the same time and can be done in a scenario that doesn't affect the production real time also it will be the same amount of time in both CGR-CNN and SVM-YCbCr, the Training time is a consumed time in the initialization of the system before start using the system in the production real time and it is different on both, but it varies based on different situations which does not affect the production real time, although if there is a need for retraining the model during the production to raise the efficiency of the system based on feedback situations, there are different fine tuning techniques in the case of CNN making it faster and easier than SVM-YCbCr also this retraining time considered an exceptional case that can be executed while the production line does not work, so it will not affect the real time production. Looking to (Image reading and Processing) and (Prediction time) these two time factors are involving in the real time of production.…”

“…Li and Sun [13] proposed the use of the LS-SVM as based and gray scale with texture as features, the experiment used (500) images for 4 kind of Coal/Gangue(Lean Coal, Shale, Coking Coal, Sandstone) from two different mines and achieved around (98.7%, 96.6%, 98.6%, 96.6%) recognition accuracy. Eshaq et al [24] a recognition system has been developed using thermal images of coal and gangue which perform feature extraction based on YCbCr and use SVM to classify the coal gangue, the proposed method achieved high classification accuracy (98.1%) for gangue and (96.6%) for coal this work well be mention frequently later as SVM-YCbCr.…”

“…In the case of coal and gangue images a problem of heterogeneous of the Coal/Gangue sources usually lead to differences in the Gray scale images leading to mis-recognition of the same object [6], this problem could be avoidable using the thermal images in Eshaq et al [24] which tested the coal and gangue in different heat degrees and analyzed the best conditions for thermal images to perform a classification test on coal and gangue, but using SVM algorithm need to perform feature extraction to prepare the inputs of the SVM which require deep analysis and usually limited to the set of analyzed factors, on the other hand using the convolutional neural network shorten the analysis process because CNN perform features extraction by itself which will not be limited to pre_analyzed factors and extract more features, based on that in this paper a CNN model has been designed to perform a recognition of coal and gangue using thermal images dataset from the Eshaq et al [24] for training the model Figure 1, the ability to operate in medium-term operational requirements (Memory size, Training time) has been taken in account so that the new model should be able to run in medium size GPU such as 4GB GPU and does not take along time for learning the model, while maintaining its ability to effectively obtain high accuracy comparing with the related work. The developed model was able to achieve better coal recognation reached 100% compared to 96.6 % for coal in Eshaq et al [24] also achieved a near gangue recognition reached 97.5% compared to 98.1% for gangue in Eshaq et al [24], so the implementing of the new model takes the following steps:…”

“…• Comparing the performance of the CGR-CNN which come in three steps: -Comparing CGR-CNN with the main research work SVM-YCbCr [24] and demonstrate the achieved improvement (sect. III-A).…”