## 1 Introduction  
The fast-paced nature of industrial mass-production demands that a substantial quantity of products be manufactured quickly while meeting the corresponding required quality standards. To ensure products meet the targeted level of quality, a rigorous quality control mechanism needs to be in place. Yet, if this process is inefficient, quality tasks may affect production, for instance, by slowing it down or quality may suffer, for instance, errors might go on undetected in the final product. In the construction industry, steel joists are one of the most widely used structural components. Steel joists are assembled using a Gas Metal Arc Welding (GMAW) process. Because of the variety of steel joist types, the process uses manual welding, which is somewhat error-prone and can lead to the omission of welds. If such defects go undetected by the manual and visual quality control process, the performance and stability of the structural element will be seriously affected, leading to possible hazardous safety issues. Traditional methods of quality control based on visual inspection have proven to be inefficient in terms of required time, accuracy, and cost. However, machine learning (ML) techniques can potentially provide an efficient monitoring system in a fast, accurate, and cost-effective way [1]. ML can be used to detect welding defects in various stages of the welding process. While most research in this domain focuses on identifying welding defects, our study pivots to the critical task of detecting the absence of welded parts. This focus is vital for ensuring structural safety, as missing welds pose significant risks to product integrity. Furthermore, the study focuses on missing welds as detecting them involves a complex process that typically relies on extensive manual labor. Automating such detection can enhance the efficiency of quality control process. In this study, we present a novel deep learning-based framework for real-time detection of missing welds in steel joists during the shop fabrication process. Our proposed framework integrates a comprehensive hardware setup, including a data-capturing station that streams real-time assembly line videos. This hardware setup is integrated with advanced computer vision algorithms and machine learning techniques that are utilized to accurately detect, track and classify the various types of connections and nodes and identify missing welds on prefabricated steel joists. Additionally, a user-interface alerts workers in real-time when a defect is detected, enabling prompt corrective actions. Research Objectives and ContributionsThis research explores state-of-the-art methods and develops a deep-learning-based framework, focusing on achieving high accuracy and reduced processing time in monitoring welding quality during the shop fabrication process. The project focuses on the detection of missing welds on prefabricated metal joists in a factory setting. The objectives are to: (1) Investigate a method for automatic real-time detection of missed welds on the assembly line, using video streams from the cameras in the data-capturing station and machine learning algorithms, (2) Investigate and propose an innovative solution to tackle challenges such as tracking, occlusion, and processing time, (3) Train and validate the network on datasets provided by the partner company and test the developed network in the real assembly line environment, and (4) Fine-tune the network parameters to further increase performance and compare the proposed network results with existing procedures. In our study, we have developed a deep learning-based framework to address the challenge of detecting missing welds in steel joists during the fabrication process. This framework incorporates a comprehensive hardware setup including a data-capturing station for streaming assembly line videos in real-time. Our approach integrates advanced computer vision techniques and machine learning algorithms to accurately detect, track, and classify various types of connections and nodes in steel joists.