Research Institute of Mine Big Data, Chinese Institute of Coal Science
Tiandi Science and Technology Co., Ltd.
State Key Laboratory for Intelligent Coal Mining and Strata Control
Jinjiaqu Coal Mine, CHN Energy Ningxia Coal Industry Co., Ltd.
Yangchangwan Coal Mine, CHN Energy Ningxia Coal Industry Co., Ltd

煤矿井下特别是采掘工作面空间狭窄、装备众多、工艺条件及环境复杂、隐蔽致灾隐患多，因此实现智能化无人操作一直是煤炭行业内的普遍需求。建立有效的面向煤矿井下应用的视觉计算理论是实现煤矿智能化无人开采的重要一环。矿井视觉计算的主要任务是针对矿井这一特定应用领域，研究煤矿井下环境的感知、描述、识别和理解模型与框架，以使智能装备具有通过图像或视频感知煤矿井下三维环境信息，增强煤矿井下环境感知能力。为了有效推进该理论与实践的结合发展，使其更好地服务于煤矿智能化建设，首先围绕煤矿井下视觉计算的基本概念，分析计算机视觉与矿井视觉计算的异同，总结提出煤矿井下视觉计算的组成架构体系。然后，详细介绍煤矿井下视觉计算所涉及的视觉感知与增强、特征提取与特征描述、语义学习与视觉理解、三维视觉与空间重建、感算一体与边缘智能等关键技术，并从矿井视频智能识别、预警与机器人定位、导航等方面简要介绍视觉计算在煤矿井下的典型应用案例。最后给出煤矿井下视觉计算的发展趋势和展望，重点总结分析了目前矿井视觉计算在煤矿井下应用中存在的关键技术难题和矿井增强现实/混合现实、平行智能采矿2种重要的发展方向。随着煤矿井下视觉计算理论的不断突破和完善，矿井视觉计算在煤矿智能化发展中必将发挥越来越重要的作用。

It has always been a common demand to stay away from the harsh environment with narrow space, numerous devices, complex operation process, and hidden hazards, and realize intelligent unmanned mining in the coal industry. To achieve this goal, it is very necessary for us to develop an effective theory of vision computing for underground coalmine applications. Its main task is to build effective models or frameworks for perceiving, describing, recognizing and understanding the environment of underground coalmine, and let intelligent equipment get 3D environment information in coalmine from images or videos. To effectively develop this theory and make it better for intelligent development of coalmine, this paper first analyzed the similarities and differences about computer vision and visual computing in coalmine, and proposed its composition architecture. And then, this paper introduced in detail the key technologies involved in visual computing in coalmine including visual perception and light field computing, feature extraction and feature description, semantic learning and vision understanding, 3D vision reconstruction, and sense computing integration and edge intelligence, which is followed by typical application cases of visual computing in coalmines. Finally, the development trend and prospect of underground visual computing in coalmine was given. In this section, this paper focused on concluding the key challenges and introducing two valuable applications including coalmine Augmented Reality/Mixed Reality and parallel intelligent mining. With the breakthrough of underground vision computing, it will play a more and more important role in the intelligent development of coal mines.

天地科技股份有限公司科技创新创业资金专项重点资助项目（2021-TD-ZD002，2022-2-TD-ZD001）；煤炭科学研究总院创新创业科技专项资助项目（2021-JSYF-004）

程　健，李　昊，马　昆，等. 矿井视觉计算体系架构与关键技术[J]. 煤炭科学技术，2023，51（9）：202−218

CHENG Jian，LI Hao，MA Kun，et al. Architecture and key technologies of coalmine underground vision computing[J]. Coal Science and Technology，2023，51（9）：202−218