矿产保护与利用2026，Vol.46Issue(1)：1-13,13.DOI:10.13779/j.cnki.issn1001-0076.2026.02.001
矿山安全隐患多模态智能感知与识别方法研究综述
A Survey on Multi-Modal Intelligent Perception and Hazard Recognition Techniques in Mine Safety
摘要
Abstract
With the advancement of the"dual-carbon"strategy and intelligent mine construction,mine safety production places increasingly high demands on reliable intelligent perception technologies.In underground environments with insufficient lighting,dust,water mist,and confined spaces,single-modal perception methods based on visible images,infrared images,depth maps,or 3D point clouds often suffer from imaging degradation and feature loss.As a result,their recognition stability is limited,making it difficult to achieve accurate perception of personnel behaviors,equipment operating states,and environmental hazards in high-risk mining scenarios.Multimodal perception,which leverages complementary information across modalities in texture,thermal and geometric characteristics,offers an effective solution for enhancing mine safety hazard recognition.This paper reviews the characteristics of multimodal data commonly used in mines and analyzes the strengths and limitations of visible cameras,infrared cameras,LiDAR,and depth cameras in information acquisition and feature representation.Three representative fusion paradigms,including data-level,feature-level,and decision-level fusion,are then summarized with their applicable scenarios.Recent progress in multimodal-based target detection and hazard recognition is reviewed,focusing on visible-infrared image fusion,visible image-point cloud fusion,and visible-depth image fusion.Coal mine scenarios are taken as representative examples,typical applications are discussed to illustrate their engineering value in personnel localization,equipment operation monitoring,and environmental condition perception.Finally,key challenges,such as annotation cost,cross-modal alignment,robustness,and lightweight deployment,are analyzed,and future directions toward mine automation,unmanned operations,and safety risk prediction are outlined.This paper aims to provide a reference and technical guidance for both theoretical studies and engineering applications of multimodal intelligent perception and recognition in mine safety analysis.关键词
矿井智能感知/多模态融合/目标检测/深度学习Key words
intelligent sensing in mines/multimodal fusion/object detection/deep learning分类
矿业与冶金引用本文复制引用
程德强,王衍辰,岳阳,田亮,翟杰,寇旗旗..矿山安全隐患多模态智能感知与识别方法研究综述[J].矿产保护与利用,2026,46(1):1-13,13.基金项目
国家重点研发计划项目(2023YFC2907600) （2023YFC2907600）
深地科学与工程云龙湖实验室资助项目(104024005) （104024005）
中央高校基本科研业务费专项资金项目(2024ZDPYCH1001) （2024ZDPYCH1001）
在徐高校服务"343"产业发展项目(gx2024004) （gx2024004）
