基于X射线图像和激光点云的煤矸识别方法

煤矸高效分选是实现煤炭资源绿色开采的重要手段，其核心技术是煤和矸石的快速精准识别。因此，本文提出了基于X射线图像和激光点云融合的煤矸识别方法。首先，设计了基于局部熵和全局均差加权的改进Otsu分割算法，以此提高X射线图像的分割精度和分割效率；同时，利用直通滤波和体素栅格降采样简化了煤矸激光点云数据，进而提取了X射线图像和激光点云的煤矸组合特征。然后，针对传统麻雀搜索算法(SSA)易陷入局部最优和种群多样性差等问题，提出了多策略改进的SSA算法(ISSA),并用于轻量梯度提升机(LightGBM)参数的寻优，进而设计了基于ISSA-LightGBM的煤矸快速识别模型。最后，搭建了煤矸识别实验平台，开展了相应的实验对比分析，结果表明：ISSA-LightGBM模型的煤矸识别准确达99.00%,综合性能优于其它模型，满足了煤矸高效识别的需求。

A coal-gangue recognition method based on X-ray image and laser point cloud

The efficient separation of coal and gangue is an important way to realize green mining of coal resources, and the core technology is the rapid and accurate identification of coal and gangue. Therefore, a coal-gangue recognition method based on the fusion of X-ray image and laser point cloud is proposed in this article. Firstly, an improved Otsu segmentation algorithm based on the local entropy and global mean difference weighting is designed to enhance the segmentation accuracy and efficiency of X-ray images. Meanwhile, the straight-through filtering and voxel grid down sampling are used to simplify the laser point cloud data of coal and gangue, and the coalgangue feature combination of X-ray image and laser point cloud is extracted. Then, to address the problems that the traditional sparrow search algorithm (SSA) is prone to fall into local optimum and the population diversity is poor, a multi-strategy improved SSA algorithm (ISSA) is proposed to optimize the model parameters of light gradient boosting machine (LightGBM). A coal-gangue fast recognition model based on ISSA-LightGBM is designed. Finally, an experimental platform for the coal-gangue recognition is established and the corresponding experimental comparative analysis is carried out. Results show that the comprehensive recognition accuracy of ISSALightGBM model can reach to 99. 00% , and the comprehensive performance is superior to other models, which could meet the needs of efficient coal-gangue recognition.