# Wavelet-Enhanced YOLO for Intelligent Detection of Welding Defects in X-Ray Films
## 5 Discussion  
The experimental results from defect-specific analysis demonstrate that WTConv significantly enhances the localization accuracy of WT-YOLO, particularly for high-risk defects such as cracks, lack of fusion, and incomplete penetration. The improvements in mAP50-95, mAP50, and mAP75 for these defects demonstrate that the multi-scale frequency domain analysis provided by WTConv effectively enables multi-scale feature extraction, which is critical for accurate defect localization. However, the slight decline in mAP for porosity indicates that WTConv may struggle with defects that exhibit subtle gray-scale variations, as frequency decomposition can sometimes alter spatial details. The improvements in recall, precision, and F1-score for cracks, lack of fusion, and incomplete penetration highlight the effectiveness of WTConv in extracting edge features and suppressing noise. The increased precision suggests that WTConv enhances the model's ability to differentiate defects from  
Figure 6: Typical defect detection result. The red and green boxes indicate the identified defect locations.  
background interference, reducing false positives. However, the slight decline in recall for slag inclusion and concave defects suggests that WTConv may miss some irregularly shaped defects or those relying on subtle shadow changes. This trade-off between precision and recall suggests that, while WTConv enhances overall detection accuracy, further refinement may be needed to fully capture all the defect types. The performance of WT-YOLO in detecting concave defects is particularly noteworthy. Although recall decreased, the improvements in precision and F1-score suggest that WTConv achieves a better balance between detecting true positives and minimizing false positives. This indicates that WTConv is particularly effective in scenarios where defect detection relies on subtle intensity variations, even if some defects are missed. The overall improvement in metrics across the seven defect categories demonstrates the robustness of WT-YOLO. Future work could focus on enhancing the model's ability to preserve spatial details during frequency decomposition, potentially through adaptive wavelet bases or hybrid frequency-space domain architectures.  
The experimental results of comparison with human inspection highlight the strengths and limitations of the WT-YOLO model in practical defect detection scenarios. The model's superior precision in detecting low-contrast defects, such as slag inclusion, porosity, and undercut, demonstrates the effectiveness of WTConv in suppressing noise and background interference. This is particularly valuable in industrial settings, where reducing false positives is critical for efficient batch screening. The model's inference speed, nearly 300 times faster than manual inspection, further highlights its potential for real-time applications, significantly improving operational efficiency. However, the lower recall rates for elongated defects, such as cracks, lack of fusion, and incomplete penetration, reveal a limitation of WTConv. While the frequency-domain filtering process effectively suppresses noise, it may result in the loss of spatial details. Compared to manual inspection, which leverages multi-scale visual focus and expert judgment, WT-YOLO struggles with detecting elongated defects, suggesting the need for additional mechanisms, such as spatial domain enhancement, to improve sensitivity. Future improvements could focus on integrating attention modules or similar spatial domain enhancement techniques to preserve critical spatial details. Notably, WT-YOLO excels in detecting blurred edge defects (e.g., undercut), while manual inspection remains superior for identifying elongated defects (e.g., lack of fusion). This indicates that a hybrid detection system combining the strengths of automated models and human expertise could offer a more comprehensive solution for industrial radiographic testing.