000			03371nas a2200409 a 4500
005			20260817162314.0
008			240223s2023 th u m tt 000 a eng d
035			_a.b12420554
099			_aAIT Thesis no.CS-23-01
100	0		_aPasuthep Vannaburana
245	1	0	_aAnalysis of building facade defect using deep segmentation models
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2023
300			_a78 leaves : _bill.
490	1		_aThesis ; _vno.CS-23-01
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Computer Science
502			_aThesis (M. Eng.) - Asian Institute of Technology, 2023
520			_aBuilding 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	_aDeep learning (Machine learning)
650		0	_aComputer-aided engineering
650		0	_aBuilding materials _xCracking
700	1		_aDailey, Matthew N., _eChairperson
700	1		_aAnwar, Naveed, _eExamination Committee
700	0		_aMongkol Ekpanyapong, _eExamination Committee
710	2		_aRoyal Thai Government, _eScholarship Donor
810	2		_aAsian Institute of Technology. _tThesis : _vno.CS-23-01
856	4	0	_3Full-Text _uhttp://203.159.5.9/ait-thesis/Viewer/viewer.php?id=B20401
907			_a.b12420554 _bmnait _ca
902			_a250303
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