The article presents an analysis of modern methods and prospects for the application of automated technologies and laser triangulation for visual inspection of weld quality in the production of large-diameter longitudinally welded pipes. A review of scientific and patent publications over the past 5 years was conducted using databases such as Google Scholar, Scopus, Web of Science, eLibrary, and Google Patents. Key aspects such as the use of laser triangulation sensors (hereafter referred to as LTS) for assessing the geometric parameters of welds and the integration of machine learning methods to enhance inspection accuracy and automation were considered. The study shows that the application of LTS in combination with machine learning methods ensures high accuracy in evaluating weld quality, which is crucial for ensuring the reliability of pipelines in various industries. Based on the conducted analysis, recommendations for developing an automated system for visual inspection of welds on production lines have been identified.

Keywords: laser triangulation, visual inspection, welds, automated technologies, machine learning, quality control, large-diameter welded pipes