This research introduces an innovative computer vision approach for enhancing the process of quality control of welds in prefabricated steel elements, a crucial component in the construction industry. Traditional inspection methods, which are often expensive and time-consuming, are replaced by a proposed system that leverages cutting-edge computer vision algorithms and deep learning techniques. Our approach involves an instance segmentation model, co-developed with an industrial partner, trained on a diverse dataset of video frames. This model is designed to segment the weld area, measure its size in pixels, and isolate the shape for subsequent defect detection, thereby facilitating the identification of overflowed or underflowed welds. Despite certain limitations, our approach significantly transforms quality control practices, resulting in increased safety, reduced risks, and structural integrity. In our experiments, the instance segmentation model achieved an average precision of 94%. Furthermore, after testing the entire framework on real data and in a real environment, the proposed framework obtained an average accuracy of 90%. The results demonstrated the effectiveness of our proposed system in enhancing weld inspection processes.