國立虎尾科技大學 |

Improving Deep Learning-Based Facade Visual Inspection : = A Data Quality Perspective.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Improving Deep Learning-Based Facade Visual Inspection :/
其他題名:	A Data Quality Perspective.
作者:	Jingjing, Guo.
面頁冊數:	1 online resource (335 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-04, Section: B.
Contained By:	Dissertations Abstracts International84-04B.
標題:	Data collection. -
電子資源:	click for full text (PQDT)
ISBN:	9798352681305

Improving Deep Learning-Based Facade Visual Inspection : = A Data Quality Perspective.
Jingjing, Guo.

Improving Deep Learning-Based Facade Visual Inspection :A Data Quality Perspective. - 1 online resource (335 pages)

Source: Dissertations Abstracts International, Volume: 84-04, Section: B.

Thesis (Ph.D.)--National University of Singapore (Singapore), 2021.

Includes bibliographical references

A building facade is an external structure to support and protect a building. Defects occurring on facade can reduce the service life of the whole building. If timely inspection and maintenance are not conducted, facade defects may cause great damages to the surroundings. Therefore, facade inspection has drawn great attention in both industry and academia.Visual inspection that assesses the facade condition is the first and a key step for periodic facade maintenance. However, traditional approaches to achieving the facade visual inspection are criticized as risky, laborious, time-consuming, and expensive. To avoid the problems in the traditional approaches, there are increasing efforts exploring to use mobile devices (i.e., unmanned aerial vehicles and robots) and artificial intelligence techniques to inspect the facade condition. Particularly, deep learning algorithms have attracted considerable research interests in recent years for structural health monitoring. Although attempts have been made to achieve defects identification with different deep learning models for various structures, many problems still existed that weaken the performance of deep learning-based facade visual inspection. Especially, the problems caused by data quality were not well addressed in previous studies.The main objective of this thesis was to improve the performance of deep learning-based facade visual inspection. In this thesis, the performance was considered from the aspects of reliability and efficiency. To achieve this objective, this thesis designed a research methodology based on the theory of total data quality management. The methodology included four phases: definition, assessment, analysis, and improvement.In the first phase, the requirement of data quality was defined. Meanwhile, the critical procedures, activities, and human factors were extracted using a Delphi study. The results of the Delphi study revealed that the procedures of data pre-processing and model construction have greater influences on the uncertainty of efficiency and the uncertainty of reliability. Next, four data quality problems, including imbalanced distribution, incomplete information, ineffective labels, and inconsistent labels, were identified in the second phase of assessment. Then, in the third phase of analysis, the potential solutions to the four data quality problems were analyzed based on the idea of managing the existent data without burdening humans. In the last phase of improvement, a research framework consisting of the corresponding solutions was proposed. The research framework was unfolded through three procedures: data selection, data annotation, and model training. For each procedure, criteria were designed to assess the data quality, and solutions were developed enabling the target stage to focus on "better" data. For data selection, a semi-supervised learning solution and an active learning solution were designed by integrating the degree of uncertainty and the degree of representativeness to utilize the unlabeled data and to enrich the labeled data. For data annotation, annotation rules for categorization, localization, and segmentation were defined in accordance with the assessment standards for facade inspection. For model training, a meta learning method was applied for solving the problem of imbalanced distribution while a rule-based deep learning method was designed to regulate the learning direction. These two methods were combined with a criterion of the similarity to the ground-truth data to provide higher weights to the highquality data. The experiment results demonstrated that the proposed solutions successfully improved the accuracy and stability of the detection of various facade defects. Besides, the detection results obtained by the proposed solutions provided more effective outcomes for condition evaluation. Meanwhile, the time and cost were saved in general perspective because no extra labor works were expended. Therefore, the reliability and the efficiency of deep learning-based facade visual inspection were effectively improved by the proposed research framework.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798352681305Subjects--Topical Terms:

1372505
Data collection.
Index Terms--Genre/Form:

554714
Electronic books.

Improving Deep Learning-Based Facade Visual Inspection : = A Data Quality Perspective.
LDR:05478ntm a22003857 4500 001 1141687
005 20240414211445.5
006 m o d
007 cr mn ---uuuuu
008 250605s2021 xx obm 000 0 eng d
020 $a 9798352681305
035 $a (MiAaPQ)AAI29352297
035 $a (MiAaPQ)USingapore218220
035 $a AAI29352297
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Jingjing, Guo. $3 1465625
245 1 0 $a Improving Deep Learning-Based Facade Visual Inspection : $b A Data Quality Perspective.
264 0 $c 2021
300 $a 1 online resource (335 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-04, Section: B.
500 $a Advisor: Qian, Wang.
502 $a Thesis (Ph.D.)--National University of Singapore (Singapore), 2021.
504 $a Includes bibliographical references
520 $a A building facade is an external structure to support and protect a building. Defects occurring on facade can reduce the service life of the whole building. If timely inspection and maintenance are not conducted, facade defects may cause great damages to the surroundings. Therefore, facade inspection has drawn great attention in both industry and academia.Visual inspection that assesses the facade condition is the first and a key step for periodic facade maintenance. However, traditional approaches to achieving the facade visual inspection are criticized as risky, laborious, time-consuming, and expensive. To avoid the problems in the traditional approaches, there are increasing efforts exploring to use mobile devices (i.e., unmanned aerial vehicles and robots) and artificial intelligence techniques to inspect the facade condition. Particularly, deep learning algorithms have attracted considerable research interests in recent years for structural health monitoring. Although attempts have been made to achieve defects identification with different deep learning models for various structures, many problems still existed that weaken the performance of deep learning-based facade visual inspection. Especially, the problems caused by data quality were not well addressed in previous studies.The main objective of this thesis was to improve the performance of deep learning-based facade visual inspection. In this thesis, the performance was considered from the aspects of reliability and efficiency. To achieve this objective, this thesis designed a research methodology based on the theory of total data quality management. The methodology included four phases: definition, assessment, analysis, and improvement.In the first phase, the requirement of data quality was defined. Meanwhile, the critical procedures, activities, and human factors were extracted using a Delphi study. The results of the Delphi study revealed that the procedures of data pre-processing and model construction have greater influences on the uncertainty of efficiency and the uncertainty of reliability. Next, four data quality problems, including imbalanced distribution, incomplete information, ineffective labels, and inconsistent labels, were identified in the second phase of assessment. Then, in the third phase of analysis, the potential solutions to the four data quality problems were analyzed based on the idea of managing the existent data without burdening humans. In the last phase of improvement, a research framework consisting of the corresponding solutions was proposed. The research framework was unfolded through three procedures: data selection, data annotation, and model training. For each procedure, criteria were designed to assess the data quality, and solutions were developed enabling the target stage to focus on "better" data. For data selection, a semi-supervised learning solution and an active learning solution were designed by integrating the degree of uncertainty and the degree of representativeness to utilize the unlabeled data and to enrich the labeled data. For data annotation, annotation rules for categorization, localization, and segmentation were defined in accordance with the assessment standards for facade inspection. For model training, a meta learning method was applied for solving the problem of imbalanced distribution while a rule-based deep learning method was designed to regulate the learning direction. These two methods were combined with a criterion of the similarity to the ground-truth data to provide higher weights to the highquality data. The experiment results demonstrated that the proposed solutions successfully improved the accuracy and stability of the detection of various facade defects. Besides, the detection results obtained by the proposed solutions provided more effective outcomes for condition evaluation. Meanwhile, the time and cost were saved in general perspective because no extra labor works were expended. Therefore, the reliability and the efficiency of deep learning-based facade visual inspection were effectively improved by the proposed research framework.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Data collection. $3 1372505
650 4 $a Experiments. $3 1372555
650 4 $a Research methodology. $3 1372463
650 4 $a Efficiency. $3 1086052
650 4 $a Questionnaires. $3 557573
650 4 $a Datasets. $3 1372549
650 4 $a Accuracy. $3 1465628
650 4 $a Robotics. $3 561941
650 4 $a Aerospace engineering. $3 686400
650 4 $a Annotations. $3 1465627
650 4 $a Algorithms. $3 527865
650 4 $a Design. $3 595500
650 4 $a Classification. $3 933953
650 4 $a Neural networks. $3 1011215
650 4 $a Active learning. $3 555387
650 4 $a Localization. $3 1091696
650 4 $a Boxes. $3 1465626
650 4 $a Unmanned aerial vehicles. $3 1372845
650 4 $a Computer science. $3 573171
650 4 $a Defects. $3 1372731
650 4 $a Deep learning. $3 1127428
655 7 $a Electronic books. $2 local $3 554714
690 $a 0389
690 $a 0984
690 $a 0800
690 $a 0538
690 $a 0454
690 $a 0771
710 2 $a National University of Singapore (Singapore). $3 1193097
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Dissertations Abstracts International $g 84-04B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29352297 $z click for full text (PQDT)