# Welding Bead Inspection Using Image and Multi-Sensor Fusion
## 4 Experimental Results
### Experiment Results
(**d**) Horizontal direction inspection of bad-4. Figure 7: The proposed inspection results of bad SRD welding bead products. (**a**) Vertical direction inspection of bad-1. (**b**) Horizontal direction inspection of bad-1. (**c**) Vertical direction inspection of bad-2. (**d**) Horizontal direction inspection of bad-2. method was confirmed to complement the drawbacks of sensor inspection and image inspection, resulting in its excellent performance and fast computational speed. To evaluate the performance of the proposed method, comparative experiments were conducted using existing methods. Among the existing methods, we conducted experiments with improved Grabcut [20], morphological geodesic active contour with Canny [13], KNN, K-means with HSV [22], and clustering with the SCA [26]. Initially, these experiments were performed on two good samples for each method, and the results are shown in Figure 9. Each method highlights detected areas in red as shown in Figure 4.0 From the experimental results, it was observed that all methods detected more areas than the actual welding bead regions. In particular, as shown in Figure 9g-j, K-means with HSV and clustering with the SCA failed to detect welding beads to a large extent. Next, we conducted experiments using existing methods on the bad images in Figure 5c-f, and the results are shown in Figure 4.1 As observed in Figure 10, similar to Figure 9, detected areas for each method are highlighted in red. As seen in Figure 10a,b, improved Grabcut performed the best in detecting the welding beads, while the other methods detected more areas than the actual welding bead region. Additionally, as shown in Figure 8, the two images that the proposed method failed to detect were also not detected using the existing methods. In particular, clustering failed to even detect the shape of the welding beads. Based on the results of the welding bead detection experiments using existing methods, we conducted classification tests for the good and bad products, and the results are presented in Tables 4 and 5. Table 4 shows the confusion matrix. As observed in Table 4, K-means with HSV had the highest number of misclassifications, misclassifying good products as bad in 109 cases. Furthermore, morphological geodesic active contour with Canny, KNN, and clustering with the SCA failed to detect any bad products. Table 5 shows the performance metrics that were calculated based on the results of Figure 4.2 Among the existing methods, improved Grabcut exhibited the highest performance metrics, followed by morphological geodesic active contour with Canny and clustering with the SCA, and then, in order, KNN and K-means with HSV. While K-means with HSV had the highest precision of 97.73, it misclassified approximately 1/4 of the good products, resulting in the lowest accuracy, recall, and F1 score values. Among the existing methods, improved Grabcut had the highest performance, but the proposed method was able to accurately classify all welding beads. Furthermore, improved Grabcut had a computation time of 690.75 s, which was considerably high. In contrast, our proposed method had the shortest computation time of 25.736 s.  
Figure 4.3 Existing algorithm results of bad SRD welding bead products. (**a**) Improved Grabcut results of bad-1. (**b**) Improved Grabcut results of bad-2. (**c**) Improved Grabcut results of bad-3. (**d**) Improved Grabcut results of bad-4. (**e**) MorphGAC with Canny results of bad-1. (**f**) MorphGAC with Canny results of bad-2. (**g**) MorphGAC with Canny results of bad-3. (**h**) MorphGAC with Canny results of bad-4. (**i**) KNN results of bad-1. (**j**) KNN results of bad-2. (**k**) KNN results of bad-3. (**l**) KNN results of bad-4. (**m**) K-means with HSV results of bad-1. (**n**) K-means with HSV results of bad-2. (**o**) K-means with HSV results of bad-3. (**p**) K-means with HSV results of bad-4. (**q**) Clustering with the SCA results of bad-1. (**r**) Clustering with the SCA results of bad-2. (**s**) Clustering with the SCA results of bad-3.