Automated detection of weld defects based on the improved Cascade Mask R-CNN

Wenming Guo; Shanshu Chen; Lihong Liang; Ruiqi Jia

doi:10.1117/12.2678627

14 April 2023 Automated detection of weld defects based on the improved Cascade Mask R-CNN

Wenming Guo, Shanshu Chen, Lihong Liang, Ruiqi Jia

Proceedings Volume 12634, International Conference on Optics and Machine Vision (ICOMV 2023); 126340Q (2023) https://doi.org/10.1117/12.2678627
Event: International Conference on Optics and Machine Vision (ICOMV 2023), 2023, Changsha, China

Abstract

In weld defect detection, due to differences in sample distribution, the single threshold-based object detection algorithms may lead to low detection accuracy when locating and identifying defects in x-ray images. To address this problem, we propose a weld defect detection method based on the cascaded structure model. More specifically, we improve Cascade Mask R-CNN by using deformable convolution, feature pyramid network, an efficient global context modeling, and self-setting the aspect ratios of anchors. In addition, we introduce the data augmentations of flipping and crop-paste to enhance the size of the dataset. Experiments show that the improved Cascade Mask R-CNN significantly realizes better detection accuracy than other classic two-stage object detection models, especially for minor defects such as round defects and cracks, and verify that the improved Cascade Mask R-CNN partially counteracts the effects of differences in the defect samples’ distribution.

Citation Download Citation

Wenming Guo, Shanshu Chen, Lihong Liang, and Ruiqi Jia "Automated detection of weld defects based on the improved Cascade Mask R-CNN", Proc. SPIE 12634, International Conference on Optics and Machine Vision (ICOMV 2023), 126340Q (14 April 2023); https://doi.org/10.1117/12.2678627

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