Publicacions CVC
Home
|
Show All
|
Simple Search
|
Advanced Search
|
Add Record
|
Import
You must login to submit this form!
Login
Quick Search:
Field:
main fields
author
title
publication
keywords
abstract
created_date
call_number
contains:
...
Edit the following record:
Author
...
is Editor
Title
...
Type
Journal Article
Abstract
Book Chapter
Book Whole
Conference Article
Conference Volume
Journal
Magazine Article
Manual
Manuscript
Map
Miscellaneous
Newspaper Article
Patent
Report
Software
Year
...
Publication
...
Abbreviated Journal
...
Volume
...
Issue
...
Pages
...
Keywords
...
Abstract
The sewerage infrastructure is one of the most important and expensive infrastructures in modern society. In order to efficiently manage the sewerage infrastructure, automated sewer inspection has to be utilized. However, while sewer defect classification has been investigated for decades, little attention has been given to classifying sewer pipe properties such as water level, pipe material, and pipe shape, which are needed to evaluate the level of sewer pipe deterioration. In this work we classify sewer pipe defects and properties concurrently and present a novel decoder-focused multi-task classification architecture Cross-Task Graph Neural Network (CT-GNN), which refines the disjointed per-task predictions using cross-task information. The CT-GNN architecture extends the traditional disjointed task-heads decoder, by utilizing a cross-task graph and unique class node embeddings. The cross-task graph can either be determined a priori based on the conditional probability between the task classes or determined dynamically using self-attention. CT-GNN can be added to any backbone and trained end-toend at a small increase in the parameter count. We achieve state-of-the-art performance on all four classification tasks in the Sewer-ML dataset, improving defect classification and water level classification by 5.3 and 8.0 percentage points, respectively. We also outperform the single task methods as well as other multi-task classification approaches while introducing 50 times fewer parameters than previous modelfocused approaches.
Address
...
Corporate Author
...
Thesis
Bachelor's thesis
Master's thesis
Ph.D. thesis
Diploma thesis
Doctoral thesis
Habilitation thesis
Publisher
...
Place of Publication
...
Editor
...
Language
...
Summary Language
...
Original Title
...
Series Editor
...
Series Title
...
Abbreviated Series Title
...
Series Volume
...
Series Issue
...
Edition
...
ISSN
...
ISBN
...
Medium
...
Area
...
Expedition
...
Conference
...
Notes
...
Approved
yes
no
Location
Call Number
...
Serial
Marked
yes
no
Copy
true
fetch
ordered
false
Selected
yes
no
User Keys
...
User Notes
...
User File
...
User Groups
...
Cite Key
...
Related
...
File
URL
...
DOI
...
Online publication. Cite with this text:
...
Location Field:
don't touch
add
remove
my name & email address
Home
SQL Search
|
Library Search
|
Show Record
|
Extract Citations
Help