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。

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.