Traditional time-consuming and error-prone methods of quality control of prefabricated elements can be replaced with state-of-the-art approaches. For example, emerging computer vision and deep learning technologies can be employed in the processes to achieve better performance and quality. To detect defaults and missing welds on prefabricated steel products in real-time, this study enhances the performance, robustness, and extensibility of the computer vision-based method previously proposed by our research team.
With the advancement of methods based on artificial-intelligence, computer vision and deep learning, activities concerning progress monitoring, safety management, and quality control can be automated that leads to saving in time and cost. With the collaboration with partner industry, computer-vision approaches are going to be employed in order to find an improved method that utilizes new machine learning techniques to detect defaults on prefabricated production line. The results will help with improving the quality level of products and reducing potential project risks.