## 8 Conclusions and Recommendations  
We developed a deep-learning-based framework that leverages an average-based classification over a sequence of frames to identify missed welds on a steel joist assembly line. Previous approaches monitor welding quality using different data sources, yet the proposed framework can detect the absence of a necessary weld within the joist connections using live RGB data. The framework consists of multiple modules to detect, track, and classify the nodes of the joists and it utilizes postprocessing techniques to remove weak predictions from the final classification. Our input image source is the live camera feed, and the framework results are both stored in an SQL database and displayed on a web-based user interface in real-time. To train the framework, a dataset of 16,147 annotated images was created with six classes of objects. Several sampling techniques were used to adjust the severely unbalanced class data, including intentionally making different configurations of missed welds. An extra class was added to the dataset for a partially or fully blocked view of a node to notify the human quality controller. Dataset validations show accurate results with mAP of 0.783 and AP\({}_{50}\) of 0.995 applying the k-fold cross-validation technique. The detector inference time was 0.0083 s and the framework time was 0.0167 s. After framework implementation in the production phase, a comparison was made between framework outputs and the factory logs for the first stage of visual quality control, leading to a precision and recall of 0.884. The detection rate of the framework versus human checkers was 2.71, meaning the developed system outperformed humans in finding weld defects. In future work, it is recommended that extra cameras be used to address the camera blind spot issue. Also, other sources of data, such as depth and thermal cameras, should be investigated. Even if we created the main types of missed weld, the dataset still lacks some minor types. This can be corrected by intentionally creating other types of missed welds. Alternatively, techniques such as data augmentation and data synthesis can be used to generate the absent types in the dataset artificially. In the case study, joists are welded while moving, yet there are other lines in the factory where the joists remain static in the welding process. Future work could focus on redesigning the current hardware and software settings to enable the system to adapt to the specifications of other lines. The framework inference time could be further decreased if the wrapper were removed from the framework. This would require that the framework architecture only use one programming language. To further improve the precision of minor classes (missed-veld), it is recommended to utilize a deep-learning-based binary classification network to confirm the existence of missed welds in the objects previously detected as missed-veld by our developed framework. The mentioned reevaluation could decrease false positives in the severely unbalanced data input. H.R. : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing--original draft, Writing--review & editing. A.S.: Validation, Visualization, Writing--original draft preparation, Writing--review & editing. D.B. : Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation, Writing--review & editing. tion, Writing--original draft preparation. A.M.: Conceptualization, Methodology, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing--original draft preparation, Writing--review & editing. All authors have read and agreed to the published version of the manuscript. This research was partially funded by Mitacs Acceleration Internship Program grants IT18375 and IT25884. The datasets presented in this article are not readily available because of data ownership restrictions. Requests to access the datasets should be directed to Groupe CANAM.