The proposed model RDIUTrans is an improved U-Net model. It consists of 5 layers of encoding and decoding paths, and a Transformer module added at its bottom. The encodin...
Abstract:
Accurate welding defect detection (WDD) of Oil/Gas pipelines (OGP) is an active and challenging task in the reliability engineering of OGPs. To solve the problems that U-...Show MoreMetadata
Abstract:
Accurate welding defect detection (WDD) of Oil/Gas pipelines (OGP) is an active and challenging task in the reliability engineering of OGPs. To solve the problems that U-Net cannot effectively extract multi-scale global context details of the image by simple skip connection, and small defects cannot be accurately detected, a WDD method of OGP by combining residual-dilated-Inception U-Net (RDIU-Net) and Transformer (RDIUTrans) is proposed. In the model, RDIU-Net is used to extract the multi-scale local features, and Transformer is utilized to model multi-scale global contextual relationships and spatial dependency. Compared with U-Net and its variants, RDIUTrans can extract the global feature and local detail features for WDD. The results on the welding defect image dataset show that RDIUTrans is effective for WDIS with the segmentation accuracy of 95.34%. It is suitable for WDD scenes with various welding defects.
The proposed model RDIUTrans is an improved U-Net model. It consists of 5 layers of encoding and decoding paths, and a Transformer module added at its bottom. The encodin...
Published in: IEEE Access ( Volume: 13)