Detection method for weld defects in time-of-flight diffraction images based on multi-image fusion and feature hybrid enhancement

The accurate recognition of defects in the time-of-flight diffraction (TOFD) images of welds is important to improve the capability and efficiency of defect detection. The existing deep learning-based defect detection methods take a single image as input, without considering the fact that technicians need to observe the image "dynamically" during its evaluation, resulting in low accuracy and credibility of the defect detection results. To address these issues, combining deep learning techniques with TOFD inspection domain knowledge, this article proposes a multi-image fusion and feature hybrid enhancement-based weld defect detection method for TOFD images, comprising three parts: a single-to-multiple image decomposition module based on gain preprocessing, multi-image feature extraction module, and weld defect detection module based on feature hybrid enhancement. The developed method can realize a "dynamically changing" feature extraction and target detection of weld defects in TOFD images. The proposed method was experimentally verified using TOFD images of welds in largescale spherical pressure tanks. This method greatly surpassed the current state-of-the-art approaches, including You Only Look Once (YOLO) v9, YOLOv10, and Real-Time DEtection TRansformer (RT-DETR), achieving a mean average precision of 82.0%, average precision for small-size targets of 45.2%, and average recall for small-size targets of 70.9%. The detection time for a single TOFD image with a resolution of 500 x 1350 pixels is 0.1287 s, satisfying the real-time requirements for weld TOFD inspection in practical engineering applications. The proposed method can also be extended to engineering applications such as intelligent detection of weld defects based on X-ray images.