煤矸石快速精准的识别对提升煤炭产能有着极大的影响，而现有的煤矸识别分选方法在分选设备、准确率以及效率上尚有不足。提出了一种基于深度学习的激光雷达双通道伪彩图像煤矸识别方法。首先，基于激光雷达距离通道信息，设定高度阈值去除目标矿石以外的干扰信息；其次，对原始点云数据降维投影，以快速获得煤矸反射强度信息和表面纹理特征；然后，对降维处理后的强度通道与距离通道进行融合，构建煤矸双通道伪彩图像数据集；在此基础上，针对伪彩数据集对经典稠密连接网络（DenseNet-121）进行优化，利用优化后的DenseNet-40网络进行模型训练及测试，测试结果表明：该模型对煤矸的识别率达94.56%，证明激光雷达采集的双通道伪彩图像在矿石识别领域具有科研和工程应用价值。

The recognition accuracy and efficiency of coal and gangue have a great impact on coal-production capacity but the existing recognition and separation methods of these minerals still have deficiencies in terms of separation equipment, accuracy, and efficiency. Herein, a coal and gangue recognition method is presented based on two-channel pseudocolor lidar images and deep learning. Firstly, a height threshold is set to remove the interference information from the target ore based on the lidar distance channel information. Concurrently, the original point-cloud data are projected in a reduced dimension to quickly obtain the reflection intensity information and surface texture features of coal gangue. The intensity and distance channels after dimensional reduction are then fused to construct the dual-channel pseudocolor image dataset for coal and gangue. On this basis, the DenseNet-121 is optimized for the pseudocolor dataset, and the DenseNet-40 network is used for model training and testing. The results show that the recognition accuracy of coal gangue is 94.56%, which proves that the two-channel pseudo-color image acquired by lidar has scientific and engineering value in the field of ore recognition.