This research is making significant contributions to both academia and industry. In the academic field, it advances deep learning-based defect detection by introducing a comprehensive framework for real-time monitoring and classification of welded nodes in steel beams. This new approach not only improves detection accuracy, but also addresses practical challenges such as tracking and processing time, which are crucial for real-world applications. The development of post-processing techniques enriches academic understanding of how to improve prediction accuracy in similar applications. In industry, this research offers a game-changing solution for prefabrication quality control. By seamlessly integrating hardware, software and machine learning techniques, it provides a practical and efficient means of detecting missing welds on the factory floor. The exceptional precision achieved through advanced deep learning algorithms and fine-tuning sets a new industry standard for welding quality control. The lessons learned from implementing and validating the framework in a production environment also serve as a valuable guide for industry professionals looking to adopt similar technologies to improve their quality control processes.