CSU-Net: Contour Semantic Segmentation Self-Enhancement for Human Head Detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F23%3A39920163" target="_blank" >RIV/00216275:25530/23:39920163 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10004558" target="_blank" >https://ieeexplore.ieee.org/document/10004558</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2022.3233419" target="_blank" >10.1109/ACCESS.2022.3233419</a>

Result language

angličtina

Original language name

CSU-Net: Contour Semantic Segmentation Self-Enhancement for Human Head Detection

Original language description

The computer vision community has made tremendous progress in solving a variety of semantic image understanding tasks, such as classification and segmentation. With the advancement of imaging technology and hardware, image semantic segmentation, through the use of deep learning, is among the most common topics which have been worked on in the last decade. However, image semantic segmentation suffers from several drawbacks such as insufficient detection of object boundaries. In this study, we present a new convolutional neural network architecture called CSU-Net that aims to self-enhance the results of semantic segmentation. The proposed model consists of two strongly concatenated encoder-decoder blocks. With this design, we reduced requirements on computing power and memory size to decrease costs and increase the training/prediction speed. This study also demonstrates the advantage of the proposed system for small training data sets. The proposed approach has been implemented on our private dataset, as well as on a publicly available dataset. A comparative analysis was carried out with four popular segmentation models and three other recently introduced architectures to show the efficiency of the proposed system. CSU-Net outperformed the other competing neural networks that we considered for the comparative study. As an example, it succeeded in improving the traditional U-Net result by approximately 50% in mean Intersection over Union (mIoU) for both tested datasets. Based on our experience, the CSU-Net can improve results of semantic segmentation in many applications.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LTAIN19100" target="_blank" >LTAIN19100: Smart contactless technology development for smart fencing</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE ACCESS

ISSN

2169-3536

e-ISSN

—

Volume of the periodical

11

Issue of the periodical within the volume

12

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

987-999

UT code for WoS article

000910176700001

EID of the result in the Scopus database

2-s2.0-85146237760