Semantic Segmentation under a Complex Background for Machine Vision Detection Based on Modified UPerNet with Component Analysis Modules

Joint Authors

Huang, Jian
Liu, Guixiong
Wang, Bodi

Source

Mathematical Problems in Engineering

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-09-12

Country of Publication

Egypt

No. of Pages

13

Main Subjects

Civil Engineering

Abstract EN

Semantic segmentation with convolutional neural networks under a complex background using the encoder-decoder network increases the overall performance of online machine vision detection and identification.

To maximize the accuracy of semantic segmentation under a complex background, it is necessary to consider the semantic response values of objects and components and their mutually exclusive relationship.

In this study, we attempt to improve the low accuracy of component segmentation.

The basic network of the encoder is selected for the semantic segmentation, and the UPerNet is modified based on the component analysis module.

The experimental results show that the accuracy of the proposed method improves from 48.89% to 55.62% and the segmentation time decreases from 721 to 496 ms.

The method also shows good performance in vision-based detection of 2019 Chinese Yuan features.

American Psychological Association (APA)

Huang, Jian& Liu, Guixiong& Wang, Bodi. 2020. Semantic Segmentation under a Complex Background for Machine Vision Detection Based on Modified UPerNet with Component Analysis Modules. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1197413

Modern Language Association (MLA)

Huang, Jian…[et al.]. Semantic Segmentation under a Complex Background for Machine Vision Detection Based on Modified UPerNet with Component Analysis Modules. Mathematical Problems in Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1197413

American Medical Association (AMA)

Huang, Jian& Liu, Guixiong& Wang, Bodi. Semantic Segmentation under a Complex Background for Machine Vision Detection Based on Modified UPerNet with Component Analysis Modules. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1197413

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1197413