  In actual industrial production, the assessment of the steel plate welding
effect is an important task, and the segmentation of the weld section is the
basis of the assessment. This paper proposes an industrial weld segmentation
network based on a deep learning semantic segmentation algorithm fused with
heatmap detail guidance and Image Matting to solve the automatic segmentation
problem of weld regions. In the existing semantic segmentation networks, the
boundary information can be preserved by fusing the features of both high-level
and low-level layers. However, this method can lead to insufficient expression
of the spatial information in the low-level layer, resulting in inaccurate
segmentation boundary positioning. We propose a detailed guidance module based
on heatmaps to fully express the segmented region boundary information in the
low-level network to address this problem. Specifically, the expression of
boundary information can be enhanced by adding a detailed branch to predict
segmented boundary and then matching it with the boundary heat map generated by
mask labels to calculate the mean square error loss. In addition, although deep
learning has achieved great success in the field of semantic segmentation, the
precision of the segmentation boundary region is not high due to the loss of
detailed information caused by the classical segmentation network in the
process of encoding and decoding process. This paper introduces a matting
algorithm to calibrate the boundary of the segmentation region of the semantic
segmentation network to solve this problem. Through many experiments on
industrial weld data sets, the effectiveness of our method is demonstrated, and
the MIOU reaches 97.93%. It is worth noting that this performance is comparable
to human manual segmentation ( MIOU 97.96%).
