Generalized weld bead region of interest localization and improved faster R-CNN for weld defect recognition

Abstract

Pipeline is the main transportation. In order to ensure the safety of pipeline transportation, the research on automatic recognition of defect features in X-ray film digital images has become an important research interest. Deep learning is highly favored via its powerful feature of self-extraction ability, which avoids the complexity and uncertainty associated with manual extraction in traditional algorithms. Nevertheless, in weld seam X-ray images, the weld bead only occupies a small portion of the entire image, and the welding defects exclusively exist within the weld bead. If the weld bead area can be determined first, followed by defect extraction within the weld bead, which will undoubtedly improve the accuracy of defect detection. Therefore, this paper first proposes a simple and general two-stage weld bead ROI (region of interest) extraction algorithm, which roughly determines weld bead ROI based on the distribution of the weld bead center and accurately locates the boundaries of the weld bead ROI based on the vertical distance curve. Then, to address the issues of Faster R-CNN (region-based convolutional neural networks) only utilizing the highest-level features and the poor adaptability of prior boxes setting, FPN (feature pyramid network) and CBAM (convolutional attention mechanism module) are incorporated to the feature extraction module and K-means clustering is employed to modify the setting of prior boxes. Experimental results conducted on two weld seam datasets demonstrate the effectiveness of the two-stage weld bead ROI extraction algorithm compared with the Otsu algorithm. The mAP (mean average precision) of defect recognition based on Faster R-CNN is 80.1%, which is greatly higher than the mAP achieved without extracting weld ROI (48.7%). In addition, experimental verification is performed on the Improved Faster R-CNN algorithm for identifying weld defects, and the cumulative improvement in mAP reached 6.8%, demonstrating the effectiveness and applicability of the proposed method.