| Citation: |
HE Lei，GUO Yongcun，ZHI Ya，et al. Accurate identification method of coal and gangue based on geometric feature constraints by DE-XRT[J]. Coal Science and Technology，2024，52（5）：262−275. DOI: 10.12438/cst.2023-1276
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The dual energy X-ray transmission identification of coal gangue still faces challenges in thickness, hardening, afterglow, and fan-shaped effects, among which the parameters for 5-150 mm wide thickness coal gangue separation fluctuate significantly and the recognition rate is to be improved. Therefore, this paper proposes a multi-dimensional identification method of dual-energy X-ray transmission of coal gangue based on geometric feature constraints. This method distinguishes the thickness of coal gangue by two geometric features of the minimum circumscribed circle diameter and area of the target image, restricts the spatial distribution of X-ray transmission response characteristics, and then weakens the influence of defects from multiple dimensions. With a small amount of low-density coal and high-density gangue, the paper obtains X-ray transmission response characteristics, position characteristics, and geometric characteristics, and combine them with Relief-F feature selection to establish a strong feature combination. To test the recognition performance of multiple classifiers, medium Gaussian SVM is selected as the classification model for multi-dimensional methods. Taking strong feature combinations as input, the final decision model and classification of unknown coal gangue pixels are automatically created, and the separation parameter p-value is obtained through pixel transformation image processing method. The results show that there is a strong linear correlation between p-value and coal gangue density, and density can be used to select p-value to regulate sorting. The p-value shows a weak linear correlation with the thickness of coal gangue. Within a wide thickness range, the p-value has a small degree of dispersion and good separability, giving separation parameters a large adjustment space. The mass experimental verification results show that the p-value of the multi-dimensional method for pre discharge gangue separation parameter is 33.01%. Using this separation parameter to identify coal gangue with different densities and coal types, the overall recognition rate reaches 99.57%. The overall recognition rate of raw coal pre discharge gangue in the thickness range of 5-150 mm is 99.37%. Compared with H-L method and R-L method, the multi-dimensional method has higher recognition rate, and the p-value calculated for coal gangue with different thickness has higher accuracy and better consistency. It demonstrates the effectiveness of multi-dimensional recognition methods under geometric feature constraints and the advantages of separation parameter regulation, which is of design reference meaning for current dual energy X-ray coal gangue separation device recognition algorithms.
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