Experiment on accurate identification of thermal image of coal-gangue mixture under a simulated dusky and wet condition
| Autor: | Pengfei SHAN, Chenwei LI, Xingping LAI, Haoqiang SUN, Xu LIANG, Xingzhou CHEN, Limei FU |
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| Jazyk: | čínština |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Meitan xuebao, Vol 49, Iss S1, Pp 483-494 (2024) |
| Druh dokumentu: | article |
| ISSN: | 0253-9993 98674595 |
| DOI: | 10.13225/j.cnki.jccs.2022.1884 |
| Popis: | As an important component of intelligent mine construction, the underground intelligent separation of coal and gangue can effectively improve the green utilization of mine resources. At present, the visible light image recognition 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 improved YOLOv5 algorithm model, this paper proposed a thermal image identification method for the coal-gangue mixed situation under dark and wet conditions. The neck part of the YOLOv5 model was changed to 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 thermal image acquisition of coal-gangue mixture was constructed to simulate the low illumination and high humidity environment in underground confined space. The contrast enhancement and edge enhancement preprocessing of the thermal image collected by the infrared camera were performed by CLAHE and LAPLACE operators. The results of thermal image identification of coal gangue mixture were systematically analyzed from different data sets, different improved 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 preprocessed image is 1.7% higher than that of the original image, and the F-Measure value is increased by 6.9%. The average accuracy mean and F-Measure of the improved YOLOv5 model reaches 80.2% and 84.6%, which are higher than 74.6% and 79.7% of the classical model, which can effectively improve the detection accuracy of coal gangue thermal image. The relative humidity of the environment is positively correlated with the recognition accuracy, 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 basis for the accurate identification of the coal-gangue mixed situation under the dark and wet conditions. |
| Databáze: | Directory of Open Access Journals |
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