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X-ray weld defect detection based on AF-RCNN


For X-ray weld defect detection, this paper proposes AF-RCNN object detection framework with the application of attention mechanism because defect images have too many small defects, and feature information of small defects are more likely to be missing during the convolution. First of all, the original weld images were cut and classified, and the images were labeled in the format of PASCAL VOC dataset to build a new weld dataset for object detection. Secondly, residual network (ResNet) was combined with feature pyramid network (FPN) as the backbone, and efficient convolutional attention module (ECAM) was applied to adaptively detail the interested target feature. Finally, CIoU loss function was introduced to increase the accuracy of anchor positioning in the regression of bounding box. The experiment results show that, comparing with traditional Faster-RCNN and SSD, AF-RCNN significantly improves in weld defects detection and classification.

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This study was supported in part by the National Natural Science Foundation of China under Grant 62073118; Central government guides local technology development funds under Grant 206Z1701G; Natural Science Foundation of Hebei Province under Grant F2019202305.

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Correspondence to Weipeng Liu.

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Recommended for publication by Commission V - NDT and Quality Assurance of Welded Products

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Liu, W., Shan, S., Chen, H. et al. X-ray weld defect detection based on AF-RCNN. Weld World 66, 1165–1177 (2022).

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  • Weld defects
  • Attention