## 5 Conclusions  
This study proposed a framework to efficiently detect defects in steel manufacturing and conduct detailed analysis using label enhancement techniques. The approach begins with binary classification using ResNet-50, followed by the generation of bounding boxes through faster R-CNN. Segmentation is then performed based on these bounding boxes, accurately identifying the location and size of defects. The experimental results show that the proposed framework effectively detects surface defects and has the potential to replace traditional, costly annotation processes.  
However, there are some limitations to this study. First, the performance of object detection for defect types such as pitted surfaces and crazing, which have fine sizes and complex patterns, still requires improvement. These defects necessitate additional feature extraction and multi-scale analysis to enhance detection accuracy. Second, the segmentation results are highly dependent on the quality of the initial mask generated from the bounding box, which may pose challenges in detecting small or complex-shaped defects. Third, the study does not fully represent the diverse defect patterns and environmental factors relevant to real-time applications in the actual manufacturing process, highlighting the need for further validation in industrial settings.  
Future research could concentrate on improving recognition capabilities for more complex defect patterns by integrating multi-scale and multi-feature deep learning models or by developing lightweight models for real-time defect detection and segmentation. Additionally, applying the proposed framework to actual steel manufacturing processes to assess its performance under diverse conditions and further optimizing it for practical process improvements would be valuable.  
This study introduces an effective framework for detecting defects and conducting precise analysis in the steel manufacturing process. Further research could improve this methodology, thereby enhancing quality control and process efficiency in the steel manufacturing industry.  
Figure 13: DeepLabv3+, GrabCut-based label enhancement results. (**a**) Original image, (**b**) bounding box, (**c**) initial mask, (**d**) result after 5 rounds, (**e**) result after 10 rounds.  
**Author Contributions:** Conceptualization, T.W.; methodology, T.W.; software, T.W.; validation, T.W.; formal analysis, T.W. and M.Y.; investigation, T.W. and S.P.; resources, T.W.; data curation, T.W.; writing--original draft preparation, T.W.; writing--review and editing, J.J.; visualization, T.W. and M.Y.; supervision, J.J.; project administration, T.W.; funding acquisition, T.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by ICT Creative Consilience Program through the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (RS-2020-II201821).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The NEU Surface Defect Database presented in this study is available in the following repositories: [[https://www.kaggle.com/datasets/kaustubhdikshit/neu-surface-defect-database](https://www.kaggle.com/datasets/kaustubhdikshit/neu-surface-defect-database)]([https://www.kaggle.com/datasets/kaustubhdikshit/neu-surface-defect-database](https://www.kaggle.com/datasets/kaustubhdikshit/neu-surface-defect-database)) (accessed on 5 May 2024).  
**Acknowledgments:** This work was supported by ICT Creative Consilience Program through the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (RS-2020-II201821).  
**Conflicts of Interest:** The authors declare no conflicts of interest.