Review on the application of machine vision perception theory and technology in coal industry
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摘要: 机器视觉技术对改善煤矿安全监测手段、提高装备自动化水平具有积极意义。详细阐述了当前煤矿智能化建设过程中基于机器视觉的不同场景和系统下的设备信息状态感知原理，综述了机器视觉感知技术在煤矿安全监测、拣选识别、煤岩识别、定位导航、运输检测、位姿检测和信息测量等方面的实践应用；分析指出未来煤矿机器视觉感知技术应深入挖掘采掘工作面机器视觉场景理解需求，构建生产全视场监视检测体系，提升多时空多维度多变量集成监测效果，改善视频自主监视告警能力，增强视觉引导能力，并形成地面生产管理运行系统的视觉资料统一化管理方式等，重点研究综采装备（群）姿态同时空测量、采掘环境动态变化感知、生产全视场监测与自主告警、煤矿机器人视觉引导控制等技术；指出煤矿机器视觉感知技术在防爆或本安型智能视觉传感器研发、高效视觉测量与分析、检测识别测量精度提升、图像高质量标注方面仍存在挑战，通过开发具有边缘计算能力的视觉传感器，构建井上下视觉分布式测量方案，实现各类复杂环境下开采信息准确识别与测量，可有效提高机器视觉感知技术在煤炭行业的更深层次融合和应用。Abstract: Machine vision technology has positively improved coal mine safety monitoring methods and enhanced equipment automation levels. This article elaborates in detail on the principles of equipment information state perception based on machine vision in different scenarios and systems during the current intelligent construction process of coal mines. It summarizes the practical applications of machine vision perception technology in coal mine safety monitoring, picking recognition, coal rock recognition, positioning navigation, transportation detection, pose detection, and information measurement. The analysis points out that in the future, coal mine machine vision perception technology should deeply explore the understanding needs of mining face machine vision scenes. It is suggested to build a production full field of view monitoring and detection system, and improve the integrated monitoring effect of multiple spatiotemporal, multi-dimensional, and multivariate. It is suggested to improve the video autonomous monitoring and alarm capability, enhance visual guidance capability, and form a unified visual data management method for ground production management and operation systems. The key research should focus on technologies such as simultaneous spatiotemporal measurement of the pose of fully mechanized mining equipment (groups), perception of dynamic changes in the mining environment, full field of view monitoring and autonomous warning for production, and visual guidance and control of coal mining robots. It is pointed out that the coal mine machine vision perception technology still has challenges in explosion-proof or intrinsically safe intelligent vision sensors, efficient methods of visual measurement and analysis, the measurement precision of detection and recognition, and high-quality image annotation. Through the development of visual sensors with edge computing capabilities, a distributed vision measurement scheme is constructed to achieve accurate recognition and measurement of mining information in various complex environments. It can effectively improve the deeper integration and application of machine vision perception technology in the coal industry.
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图 4 机器视觉感知技术在煤炭工业领域应用场景
Figure 4. Application scenes of machine vision perception technology in coal industry field
图 6 基于机器视觉的煤岩分界识别效果
Figure 6. Recognition effect of coal-rock boundary based on machine vision
图 7 基于机器视觉的带式输送机故障识别
Figure 7. Belt conveyor fault recognition based on machine vision
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