Method based on a multi-image feature fusion model for detecting weld defects in time-of-flight diffraction images

The time-of-flight diffraction (TOFD) technology is widely used in weld defect inspection and the utilisation of TOFD images in automatic defect recognition is attracting interest from enterprises. However, the extant artificial intelligence (AI)-based object detection methods have limitations: they use only a single image, the ability to extract defect features is weak and they are easily affected by interference fringes. These limitations result in high miss and false detection rates. In this study, a weld defect object detection method based on a multi-image feature fusion model (MIFFM) is proposed. The original TOFD input image was preprocessed using adaptive Gaussian filtering (AGF) and anisotropic filtering (specifically, Perona-Malik (P-M) filtering) to remove noise and enhance the defect features in the image. Subsequently, the AGF preprocessed, P-M filtering preprocessed and original TOFD images were stacked and used as input images. To enhance the detection performance, the you only look once X (YOLOX) bidirectional feature fusion network combined with the non-monotonic dynamic Scylla-intersection over union (SIoU) loss function was constructed. Finally, the feasibility of the proposed method was verified using the TOFD image of a large-pressure spherical tank weld. The proposed method achieved a mean average precision (mAP) of 78.05%, which is higher than YOLOX by 4.59%.