Research status and development trend of visual processing technology for fully mechanized excavation systems
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摘要: 机器视觉技术具有非接触测量、获取信息量大、数据处理能力强等优点，将其应用于综掘工作面，对于提高综掘工作效率、保障人员设备安全、减少事故发生具有重要意义。综述了近年来视觉处理技术在煤矿综掘系统中的具体应用与发展情况，依据综掘工作面的任务分工，结合具体实际案例，重点分析了机器视觉技术在视觉检测与定位、安全监测与事故预防、装备自动化与智能化等方面的应用。通过分析不同应用场景中各类视觉检测系统的结构与检测原理，明确了视觉处理技术在综掘工作面工程应用中的技术性能、工作流程及优缺点。分析了视觉技术在综掘工作面应用中存在的挑战，包括环境适应性问题、成像视野范围较窄、智能算法的鲁棒性和可靠性尚待提高等。指出多传感器信息融合技术、设备群协同控制技术与数字孪生驱动远程监控技术是基于机器视觉的煤矿智能化装备体系未来需要重点发展的新方向。Abstract: Machine vision technology has the advantages of non-contact measurement, large amount of information acquisition, and strong data processing capability. Applying it to fully mechanized excavation faces is of great significance for improving the efficiency of fully mechanized excavation work, ensuring the safety of personnel and equipment, and reducing accidents. This article summarizes the specific application and development of visual processing technology in coal mine fully mechanized excavation systems in recent years. Based on the task division of fully mechanized excavation working faces and combined with specific practical cases, this paper focuses on the analysis of the application of machine vision technology in visual inspection and positioning, safety monitoring and accident prevention, and equipment automation and intelligence. By analyzing the structures and detection principles of various visual detection systems in different application scenarios, the technical performance, workflow, and advantages and disadvantages of visual processing technology in the application of fully mechanized excavation face engineering are clarified. This study analyzes the challenges of visual technology in the application of fully mechanized excavation face, including environmental adaptability issues, narrow imaging field of view, and the need to improve the robustness and reliability of intelligent algorithms. It is pointed out that multi-sensor information fusion technology, equipment group cooperative control technology and digital twin-driven remote monitoring technology are the new directions that need to be focused on in the future development of the intelligent equipment system of coal mine based on machine vision.
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图 1 不同文献中的悬臂式掘进机位姿检测透视投影模型
Figure 1. Perspective projection models for the position and posture detection of the boom-type roadheader in different literatures
图 2 悬臂式掘进机截割头位姿视觉检测模型
Figure 2. Visual detection model for the position and posture of the boom-type roadheader's cutting head
图 5 围岩变形监测间接标志物
Figure 5. Indirect indicators for surrounding rock deformation monitoring
图 6 输送带纵向撕裂的视觉检测方法
Figure 6. Visual detection methods for longitudinal tearing of conveyor belts
表 1 各类视觉传感器的工作原理与常见应用场景
Table 1. Working principles and common application scenarios of various visual sensors
视觉传感器 基本原理 优势 劣势 实物 综掘工作面应用 单目视觉系统 小孔成像，利用尺度不变性确定深度 结构简单、成本低、便于标定和识别 通过单张图片无法确定物体的真实大小 
KBA127矿用隔
爆型摄像仪位姿检测[4]、目标识别[5]、参数检测[6]、环境监测[7]、设备状态检测[8]等 双目视觉系统 双光热像仪 基于红外热辐射原理，可同时获取场景的可见光信息和温度信息 能见度不受限制、宽波段测量 分辨率有限、成本较高 
YRH600B矿用
防爆热像仪人员安全监测[9]、设备状态监测[10]、环境安全评估[11]等 立体视觉相机 由2个单目相机组成，利用三角测量原理估计像素空间坐标 可获取深度信息、基线距越大量程越远 工作视场小、配置与标定复杂、实时性差 
STEEReoCAM
立体相机位姿检测[12]、参数测量[13]、巷道三维重建[14]等 多目视觉系统 由多个单目相机从多视点获取同一个目标场景，基于双目测量原理重构像素空间坐标 获取信息丰富、包含冗余信息、解决了双目视觉系统匹配的多义性、匹配与定位精度高 配置与标定更加烦琐、匹配算法更复杂、计算量更大、实时性更差 
NileCAM81_
CUOAGX
多摄像头系统几何参数测量[15]、物体表面重建[16]、巷道三维重建[17]、轨迹预测[18]等 结构光相机 由相机和投影装置组成，利用红外结构光/ToF原理，通过发射接收光测距 直接获取RGB图像和深度图像、测量精度高 测量范围窄、噪声大、视野小、易受光源干扰、无法测量透射材质 
Realsense 深度
相机 D435导航定位[19]、SLAM[20]、参数测量[21]、行人识别[22]、物体表面三维重建[23]等 
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表 2 机器视觉在安全监测与事故预防中的应用分析
Table 2. Application analysis of machine vision in safety monitoring and accident prevention
应用场景 现有方法及原理 存在问题 解决方法 围岩变形监测 直接法直接拍摄巷道图像，经特征提取与点云配准，重构巷道三维信息；间接法将人工特征标志物固定在巷道表面，通过标志物变化，间接反映巷道变形 直接法监测精度高，但数据处理量大；间接法处理速度快，但数据较为稀疏 将2种方法相结合，提高监测精度与效率 火灾监测 通过提取图像中的火焰、烟雾等特征，或借助红外成像技术，使用机器学习算法或深度学习模型对提取的特征进行分析和识别，以确定是否存在火灾 煤矿火灾具有偶然性，对于早期火灾的识别，精准度与快速性有待提高 借助多源信息融合技术，将视频检测信息与多传感器信息融合，实现火灾预判 人员安全监测 通过图像处理技术识别人体特征，如头部、肢体轮廓、姿势等，分析人员行为动态，监测潜在安全风险和异常情况；或将人员位置和状态信息与预设的安全规则进行比较，触发相应的实时监控和报警机制 井下能见度低、空间狭窄、大型装备多，图像背景复杂多变，干扰人员检测的准确性 结合声音、红外等传感器，实现对矿井环境和人员的多维度监测，提高整体监测可靠性 输送带异常监测 采用可见光成像、红外热成像、结构光成像等，经特征提取与目标识别，实现输送带异物、撕裂、跑偏等异常状态监测 受低照度、高矿尘等环境因素影响，目标识别的准确度难以得到保证 深度学习等智能算法的应用可有效提高识别准确度
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表 3 机器视觉在装备自动化与智能化中的应用分析
Table 3. Application analysis of machine vision in equipment automation and intelligence
应用
场景方法原理 当前进展 存在问题 掘进
系统通过特征提取与参数解算，对巷道掘进设备进行监测和控制，实时获取设备状态、位置及可能存在的故障或异常情况，并为掘进机械提供实时导航信息 实现了基于机器视觉的悬臂式掘进机机身定位与截割头位姿检测、掘进工作面稳定性与岩层变化监测预警等；研制了新型掘进类机器人和系统，如护盾式智能掘进机器人系统、综掘巷道掘进机器人系统、煤矿大断面智能快速掘锚成套装备、煤矿高阶智能快速掘进系统等 掘进装备体积较大、结构复杂，成像装置不易安装，光路易被遮挡，会导致成像不完整或质量不佳，影响机器视觉在巷道掘进系统中的应用效果 锚护
系统通过图像处理提取锚护装备的关键特征及支护参数，以识别可能存在的安全隐患或为锚护控制提供参数依据 应用视觉技术，在传统锚护工艺的基础上进行自动化改进，如通过监测支护参数变化提高支护精度，通过智能识别钢带锚孔实现锚具精准控制等；开发出掘锚一体化机、钻锚机器人、浆液喷护机器人等新型装备 锚护系统的检测通常需要实时响应，但图像处理和识别的速度可能受到环境因素、硬件性能和算法复杂度的限制 转载运输系统 通过视觉技术识别输送设备参数、状态、障碍物等，实现转载运输系统的自动化控制和智能化管理，提高运输效率和安全性 通过煤流量检测实现运输系统优化节能，通过目标识别监测运输装备的实时位置和状态，实现运输智能调度；实现运输装备机器人化，开发出钢丝绳牵引式机器人巡检系统、带式输送机检测机器人、煤矸自动分拣机器人等 如何应用多传感器融合技术进一步提高运输装备机器人化的智能程度，保障系统对复杂环境的适应能力
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