Traditional methods of supervision in the construction industry are time-consuming and costly, requiring significant investments in skilled labor. However, with advancements in artificial intelligence, computer vision, and deep learning, these methods can now be automated, resulting in time and cost savings, as well as improvements in product quality. This research focuses on the application of computer vision approaches to monitor the quality of welding in prefabricated steel elements. A high-performance network was designed, consisting of a video capturing station, a customized classifier based on a YOLOv4 detector and an IoU tracker, and a user interface software for any interaction with quality control workers. The network demonstrated over 98% accuracy in identifying steel connection types and detecting missed welds on the assembly line in real-time. Extensive validation was conducted using a large dataset from a real production environment. The proposed framework aims to reduce rework, minimize hazards, and enhance product quality. This research contributes to the automation of quality control processes in the construction industry.