Abstract
In automatic non-destructive testing (NDT), weld bead tracking is usually performed outside. However, irregular weld boundaries, unconstrained illumination, and rough metal surfaces can cause noise, which increases the difficulty of seam tracking. In this paper, a method of parallel structured light (PSL) sensing based on deep learning and information fusion is proposed to detect weld lines. First, a camera is used to capture the laser stripe image projected by the PSL on the weld bead. Then, a MobileNet-SSD deep learning model is trained to extract the regions of interest (ROIs) to de-noise the laser stripe image. Finally, the weld line is obtained by fusing information from multiple weld boundaries.
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