# Welding Bead Inspection Using Image and Multi-Sensor Fusion
## 5 Conclusions  
In welding bead inspection, sensor-based inspection offers high accuracy in determining the presence of welding but cannot pinpoint the exact location of the welding bead. On the other hand, 2D image inspection can identify the location of the welding bead but may have a lower level of inspection accuracy compared to sensor-based inspection and involves significant computational costs. To address these limitations, this paper proposed a welding bead defect inspection method that combines sensor-based inspection and 2D image inspection. In sensor-based inspection, a method was proposed to determine defect presence based on patterns in average current, average voltage, and mixed gas flow. In image inspection, the image inspection method based on image projection was utilized [16]. The proposed inspection method, if determined as bad in sensor-based inspection, skips the image inspection step and ultimately classifies it as bad, reducing unnecessary computation and inspection times. Experimental results showed that accuracy in sensor-based and image inspections was 99.8% and 99.59%, respectively, while the combined method achieved 100% accuracy. This demonstrates that the proposed method enhances inspection accuracy by overcoming the limitations of each individual method. Furthermore, comparative experiments evaluating the proposed method's performance showed that among the existing methods, improved Grabcut achieved the highest accuracy at 98.98%. However, when compared to the proposed method, it had the highest performance and computational speed. In future work, we plan to conduct research aimed at implementing an automation system that can be applied in real industrial settings using the proposed method. Additionally, we intend to experiment with different welding products to generalize the proposed method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \multicolumn{4}{c}{**Classification**} \\ \cline{2-5}  & **TN** & **FP** & **FN** & **TP** \\ \hline Proposed algorithm & 12 & 0 & 0 & 480 \\ \hline Improved Grabcut [20] & 7 & 4 & 0 & 480 \\ \hline Morphological geodesic active & 0 & 12 & 0 & 480 \\ contour with Canny [13] & 0 & 12 & 1 & 479 \\ \hline KNN & 0 & 12 & 1 & 479 \\ \hline K-means with HSV [22] & 3 & 9 & 109 & 371 \\ \hline Clustering with the SCA [26] & 0 & 12 & 0 & 480 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A confusion matrix of the proposed and comparison algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1 score** & **Time (s)** \\ \hline Proposed algorithm & 100 & 100 & 100 & 100 & 25.736 \\ \hline Improved Grabcut [20] & 98.98 & 99.17 & 100 & 99.58 & 690.75 \\ \hline Morphological geodesic active & 97.56 & 97.56 & 100 & 98.76 & 53.88 \\ contour with Canny [13] & 97.36 & 97.56 & 99.79 & 98.66 & 186.72 \\ \hline K-means with HSV [22] & 76.02 & 97.63 & 77.29 & 86.28 & 43.15 \\ \hline Clustering with the SCA [26] & 97.56 & 97.56 & 100 & 98.76 & 1053.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification evaluation metrics and computation times of the proposed and comparison algorithms. **Author Contributions:** Conceptualization, J.L. and J.K.; methodology, J.L. and J.K.; software, J.L. and J.K.; validation, J.L., H.C. and J.K.; formal analysis, J.L. and J.K.; investigation, J.L. and J.K.; resources, J.L. and J.K.; data curation, J.L. and J.K.; writing--original draft preparation, J.L. ; writing--review and editing, H.C. and J.K.; visualization, J.K.; supervision, J.K.; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Small and Medium Business Technology Innovation Development Project from TIPA, grant number 00220304 and 00278083, the National Research Foundation of Korea, grant number CD202112500001, and Link 3.0 of PKNU, grant number 202310810001. **Institutional Review Board Statement:** Not applicable.