Normal view MARC view ISBD view

Analysis of building facade defect using deep segmentation models (Record no. 4619)

MARC details
000 -LEADER
fixed length control field	03371nas a2200409 a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20260817162314.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	240223s2023 th u m tt 000 a eng d
035 ## - SYSTEM CONTROL NUMBER
System control number	.b12420554
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Classification number	AIT Thesis no.CS-23-01
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name	Pasuthep Vannaburana
245 10 - TITLE STATEMENT
Title	Analysis of building facade defect using deep segmentation models
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc.	Pathum Thani, Thailand :
Name of publisher, distributor, etc.	Asian Institute of Technology,
Date of publication, distribution, etc.	2023
300 ## - PHYSICAL DESCRIPTION
Extent	78 leaves :
Other physical details	ill.
490 1# - SERIES STATEMENT
Series statement	Thesis ;
Volume/sequential designation	no.CS-23-01
500 ## - GENERAL NOTE
General note	A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Computer Science
502 ## - DISSERTATION NOTE
Dissertation note	Thesis (M. Eng.) - Asian Institute of Technology, 2023
520 ## - SUMMARY, ETC.
Summary, etc.	Building crack identification and structural crack assessment is an important task for structural inspection, since the early detection helps reduce the risk of loss. The traditional crack detection can be difficult, time consuming, and in case of high-rise buildings, dangerous, not to mention costly. Since detection relies on the experience of specialists, manual inspection can also be biased or inaccurate, therefore a method that utilizes automated machine vision for detecting and evaluating building cracks has been proposed as one way of improving or complementing traditional manual inspection for detecting the cracks in buildings. Because cracks on different structural elements have different causes and effects, each input image is first fed to a semantic segmentation model that outputs an image in which each building component such as column, wall, or beam, is labeled. Each output is then fed to an instance segmentation model that outputs an image in which each crack is labeled and masked with a color. The models will not only detect cracks in images but also give the instance mask segmentations of the cracks along with the building elements the cracks occurred on, which will help provide the useful information such as width, position, configuration, severity level and corrective action of cracks as a report. This study demonstrates that one can incorporate deep learning in an automated or partially automated system to evaluate cracks in building surfaces. It offers a fresh outlook on the possibility of using images automatically for building inspection and monitoring.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element	Deep learning (Machine learning)
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element	Computer-aided engineering
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element	Building materials
General subdivision	Cracking
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name	Dailey, Matthew N.,
Relator term	Chairperson
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name	Anwar, Naveed,
Relator term	Examination Committee
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name	Mongkol Ekpanyapong,
Relator term	Examination Committee
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element	Royal Thai Government,
Relator term	Scholarship Donor
810 2# - SERIES ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element	Asian Institute of Technology.
Title of a work	Thesis :
Volume/sequential designation	no.CS-23-01
856 40 - ELECTRONIC LOCATION AND ACCESS
Materials specified	Full-Text
Uniform Resource Identifier	<a href="http://203.159.5.9/ait-thesis/Viewer/viewer.php?id=B20401">http://203.159.5.9/ait-thesis/Viewer/viewer.php?id=B20401</a>
907 ## - LOCAL DATA ELEMENT G, LDG (RLIN)
a	.b12420554
b	mnait
c	a
902 ## - LOCAL DATA ELEMENT B, LDB (RLIN)
a	250303
998 ## - LOCAL CONTROL INFORMATION (RLIN)
Operator's initials, OID (RLIN)	0
Cataloger's initials, CIN (RLIN)	240311
First date, FD (RLIN)	m
--	a
--	a
--	0
945 ## - LOCAL PROCESSING INFORMATION (OCLC)
l	mnarc
945 ## - LOCAL PROCESSING INFORMATION (OCLC)
l	mnarc
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type	67-Electronic Resource
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type	40-Archives
909 ## - LOCAL ITEMS USED
Barcode	Barcode : -
CREATED	CREATED : 2024-02-23
RECORD Id	RECORD # : i13486603
LPATRON	LPATRON : 0
LCHKIN	LCHKIN : -
RENEWALS	# RENEWALS : 0
--	# OVERDUE : 0
--	IUSE3 : 0
--	TOT CHKOUT : 0
--	TOT RENEW : 0
909 ## - LOCAL ITEMS USED
Barcode	Barcode : 30050120900435
CREATED	CREATED : 2025-03-03
RECORD Id	RECORD # : i13535225
LPATRON	LPATRON : 0
LCHKIN	LCHKIN : -
RENEWALS	# RENEWALS : 0
--	# OVERDUE : 0
--	IUSE3 : 0
--	TOT CHKOUT : 0
--	TOT RENEW : 0

Holdings
Withdrawn status	Lost status	Damaged status	Not for loan	Home library	Current library	Shelving location	Date acquired	Total checkouts	Full call number	Date last seen	Price effective from	Koha item type	Barcode	Copy number
			Available for Loans	Asian Institute of Technology Library	Asian Institute of Technology Library	Archives	17/08/2026		AIT Thesis no.CS-23-01	17/08/2026	17/08/2026	67-Electronic Resource
			Available for Loans	Asian Institute of Technology Library	Asian Institute of Technology Library	Archives	17/08/2026		AIT Thesis no.CS-23-01	17/08/2026	17/08/2026	40-Archives	30050120900435	1

Analysis of building facade defect using deep segmentation models (Record no. 4619)

AIT

Asian Institute of Technology Library