Welding monitoring technology based on machine vision has been widely researched in academic and industry, especially in the background of Industry 4.0, in that it can contribute to welding quality and productivity improvement. This paper outlines the technical points of welding status monitoring based on machine vision, including hardware and software. First of all, in the hardware part, the active and passive vision systems are briefly introduced, as well as the key steps in experimental deployment, such as the configuration of optical sensors and optical filters based on different detection objects. Secondly, some related image processing techniques in welding monitoring are also comprehensively reviewed. Additionally, the observed objects and their morphological characteristics of vision-based welding process monitoring are enumerated. On this basis, a series of intelligent models as well as optimization methods for recognition and classification in visual monitoring are considered in detail. Finally, potential research challenges and open research issues of welding visual monitoring are discussed to present an insight into future research opportunities. The main purpose of this paper is to provide a reference source for the researchers involved in intelligent robot welding.

基于机器视觉的焊接监控技术在学术界和工业界得到了广泛的研究，特别是在工业4.0的背景下，可以为焊接质量和生产率的提高做出贡献。本文概述了基于机器视觉的焊接状态监测技术要点，包括硬件和软件。首先，在硬件部分，简要介绍了主动和被动视觉系统，以及实验部署的关键步骤，如基于不同检测对象的光学传感器和光学滤波器的配置。其次，对焊接监控中的一些相关图像处理技术也进行了全面的回顾。此外，还列举了基于视觉的焊接过程监控的观察对象及其形态特征。在此基础上，详细考虑了视觉监控中识别分类的一系列智能模型和优化方法。最后，讨论了焊接视觉监控的潜在研究挑战和开放研究问题，以深入了解未来的研究机会。本文的主要目的是为从事智能机器人焊接的研究人员提供参考来源。