巷道掘进过程中煤岩识别技术是掘进机截割头自动调整的核心，同样是制约矿山智能化建设的关键难题之一。针对当前采掘失衡，掘进工作面缺乏成熟有效的煤岩识别方案，现有基于图像的煤岩识别模型存在分割精度差、无法灵活部署等问题，提出一种应用在掘进工作面下基于图像分割的煤岩截割界面感知与精准识别方法。该方法结合掘进工作面实际截割情况，采用MobileNetV2特征提取网络作为DeepLabV3+的主干网络，使模型更好地兼顾分割精度和模型复杂度；将空洞空间卷积池化金字塔模块输出的高级特征进行通道注意力(SE)操作，分配通道权重以强化对重点特征信息的训练；在主干网络输出的浅层特征引入通道空间注意力(CA)机制，使浅层特征图中的低级表征信息加权，从而设计出融合双注意力机制于DeepLabV3+的煤岩截割界面识别模型。同时搭建煤尘环境下煤岩识别实验平台模拟掘进机截割后形成的煤岩截割面，研发巷道掘进过程中煤岩截割界面采集方法，并以实际矿井的掘进工作面为工程背景，验证该煤岩识别模型的分割精度以及实际应用性。研究结果表明：SE-CA-DeepLabV3+模型的平均交并比和平均像素精度分别为97.15%和98.51%，相较于其他模型具有更优的分割性能。将所建立模型对来自陕北试验矿井掘进工作面的原始煤岩图像进行验证，平均误差为0.7%，每秒传输帧数为43 fps，满足井下现场应用部署条件。

The coal-rock identification technology in the roadway excavation process is the core of the automatic adjustment of roadheader’s cutting head, and it is also one of the key problems restricting the development of intelligent mines. In view of the current mining imbalance, the excavation face lacks a mature and effective coal-rock identification scheme, and the existing image based coal-rock identification models have problems such as poor segmentation accuracy and inability to flexibly deploy, a coal-rock cutting interface perception and precise recognition method based on image segmentation is proposed in the heading face. This method combines the actual cutting situation of the excavation working face and uses the MobileNetV2 feature extraction network as the backbone network of DeepLabV3+, so that the model can better balance the segmentation accuracy and model complexity. The channel attention (SE) operation is performed on the advanced features output by the Atrous Spatial Pyramid Pooling module, and channel weights are assigned to strengthen the training of key feature information. The channel spatial attention (CA) mechanism is introduced into the shallow features output by the backbone network to weight the low-level representation information in the shallow feature map, thus designing a coal-rock cutting interface identification model that integrates the double attention mechanism in DeepLabV3+. At the same time, an experimental platform for coal-rock identification in a dusty environment is built to simulate the coal and rock cutting surface formed by the roadheader after cutting, and the coal-rock cutting interface acquisition system in the process of roadway excavation is developed. Taking the actual mine excavating face as the engineering background, the recognition accuracy and practical applicability of the coal-rock identification model are verified. The research results show that the average intersection ratio and average pixel accuracy of the SE-CA-DeepLabV3+ network model are 97.15% and 98.51%, respectively, which have better segmentation performance than other network models. The established model is used to verify the original coal and rock images from the heading face of the experimental mine in northern Shaanxi, the average error is 0.7%, and the number of transmission frames per second is 43fps, which meets the deployment conditions of downhole field applications.