Wavelet-Enhanced YOLO for Intelligent Detection of Welding Defects in X-Ray Films

Welding defects threaten structural integrity, demanding efficient and accurate detection methods. Traditional radiographic testing defects interpretation is subjective, necessitating automated solutions to improve accuracy and efficiency. This study integrates wavelet transform convolutions (WTConv) into YOLOv11n, creating WT-YOLO, to enhance defect detection in X-ray films. Wavelet transforms enable multi-resolution analysis, extracting both high-frequency and low-frequency features critical for detecting various welding defects. WT-YOLO replaces standard convolutional layers with WTConv, improving multi-scale feature extraction and noise suppression. Trained on 7000 radiographic images, WT-YOLO achieved a 0.0212 increase in mAP75 and a 0.0479 improvement in precision compared to YOLOv11n. On a test set of 200 images per defect category across seven defect types, WT-YOLO showed precision improvements of 0.0515 for cracks, 0.0784 for lack of fusion, 0.0067 for incomplete penetration, 0.1180 for concavity, 0.0516 for undercut, and 0.0204 for porosity, while experiencing a slight 0.0028 decline for slag inclusion. Compared to manual inspection, WT-YOLO achieved higher precision for cracks (0.0037), undercut (0.1747), slag inclusion (0.1129), and porosity (0.1074), with an inference speed 300 times faster than manual inspection. WT-YOLO enhances weld defect detection capabilities, providing the possibility for a robust solution for industrial applications.