DeepAI
Log In Sign Up

SHDM-NET: Heat Map Detail Guidance with Image Matting for Industrial Weld Semantic Segmentation Network

07/09/2022
by   Qi Wang, et al.
0

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 to human manual segmentation ( MIOU 97.96

READ FULL TEXT

page 1

page 3

page 6

page 7

page 8

page 9

page 12

08/05/2021

Attention-based fusion of semantic boundary and non-boundary information to improve semantic segmentation

This paper introduces a method for image semantic segmentation grounded ...
11/21/2022

Semantic Segmentation for Fully Automated Macrofouling Analysis on Coatings after Field Exposure

Biofouling is a major challenge for sustainable shipping, filter membran...
03/06/2021

Learning Statistical Texture for Semantic Segmentation

Existing semantic segmentation works mainly focus on learning the contex...
01/12/2019

Boundary-Aware Network for Fast and High-Accuracy Portrait Segmentation

Compared with other semantic segmentation tasks, portrait segmentation r...
11/21/2022

SLLEN: Semantic-aware Low-light Image Enhancement Network

How to effectively explore semantic feature is vital for low-light image...
08/01/2021

Boundary Knowledge Translation based Reference Semantic Segmentation

Given a reference object of an unknown type in an image, human observers...
05/14/2017

Gland Segmentation in Histopathology Images Using Random Forest Guided Boundary Construction

Grading of cancer is important to know the extent of its spread. Prior t...