SHDM-NET: Heat map detail guidance with image matting for industrial weld semantic segmentation network

Abstract

Welding is widely used in metal components. The firm of weld components is very important in different applications, such as buildings, bridges, cars and airplanes, etc. Weld seam quality inspection is essential to ensure product quality. The area and shape of the weld seam are the basis for quality assessment. So the segmentation of the weld area is very important for quality assessment. To address the problem of segmentation of the weld seam region, a weld seam segmentation network based on heat map detail guidance with Matting is proposed in this paper, which provides a new idea for fine-grained segmentation of the weld seam region. The existing DCNN-based semantic segmentation algorithm model has a poor segmentation effect at the boundary and jagged segmentation boundary, which are unacceptable for the weld segmentation problem that requires clear and precise boundary positioning. To solve this problem, three innovations are made in this paper on the DCNN-based semantic segmentation network. (1) A heat map detail guidance module makes the segmentation boundary information focus on shallow features and enhances the representation of boundary information. (2) A segmentation head improvement method for fine-grained semantic segmentation is proposed. (3) In response to the loss of process details in the coding and decoding of the semantic segmentation network, resulting in poor segmentation boundary accuracy, this paper introduces a matting algorithm to calibrate the boundary of the weld seam segmentation region. Through many experiments on industrial weld data sets, the effectiveness of our method is demonstrated, and the MIoU (Mean Intersection over Union) reaches 96.32%. It is worth noting that this performance is comparable to human manual segmentation ( MIoU 96.38%).