Experiment on accurate identification of thermal image of coal-sgangue mixture under a simulated dusky and wet condition

SHAN Pengfei; LI Chenwei; LAI Xingping; SUN Haoqiang; LIANG Xu; CHEN Xingzhou; FU Limei

doi:10.13225/j.cnki.jccs.2022.1884

Abstract：As an important component of intelligent mine construction,the underground intelligent separation of coal gangue can effectively improve the green utilization of coal resources.At present,the visible light image recog-nition technology for the identification of coal-gangue mixture in dark and humid underground environment remains to be improved.Based on the thermal infrared imaging technology and the improved YOLOv5 algorithm model,this paper proposed a thermal image identification method for coal-gangue mixed situation under dark and wet conditions.The Neck part of the YOLOv5 model was changed to the Bidirectional Feature Pyramid Network(BiFPN)structure,and the identification efficiency of coal-gangue was improved through multi-level feature fusion.The CIOU function was used as the loss function to improve the accuracy of coal-gangue detection.An experimental platform for the ther-mal image acquisition of coal-gangue mixture was constructed to simulate the low illumination and high humidity envi-ronment in underground confined space.The contrast enhancement and edge enhancement preprocessing of the thermal image collected by the infrared camera were performed by the CLAHE and LAPLACE operators.The results of thermal image identification of coal gangue mixture were systematically analyzed from different data sets,different im-proved modules and different algorithm models,and the influence of humidity change on the accuracy of coal-gangue identification under dark and wet conditions was explored.The results show that the average accuracy of the prepro-cessed image is 1.7%higher than that of the original image,and the F-Measure value is increased by 6.9%.The aver-age accuracy mean and the F-Measure of the improved YOLOv5 model reach 80.2%and 84.6%,which are high-er than 74.6%and 79.7%of the classical model,which could effectively improve the detection accuracy of coal gangue thermal image.The relative humidity of the environment is positively correlated with the recognition accu-racy,and negatively correlated after the humidity reaches a certain threshold.It is proposed that the thermal image can accurately identify the coal-gangue mixture in the dark and humid closed environment,which provides a scientific ba-sis for the accurate identification of the coal-gangue mixed situation under the dark and wet conditions.