Using digital image processing and Unmanned Aerial Vehicle (UAV) for identifying ceramic cladding detachment in building facades

Sousa, Alcineide Dutra Pessoa de; Sousa, Gean Carlos Lopes de; Maués, Luiz Maurício Furtado

doi:10.1590/s1678-86212022000200601

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…They achieved significant improvements in deblurring performance. Sousa et al [11] utilized YOLO (you only look once) algorithm to inspect ceramic cladding detachment in building facades and achieved 94% precision and 75% recall in the test experiment. The above research work focus on single type of defect (crack or ceramic cladding detachment) inspection.…”

Section: Introductionmentioning

confidence: 99%

Defect Detection in Civil Structure Using Deep Learning Method

Zhou,

Tiong

2024

J. Phys.: Conf. Ser.

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With the usage time, undesirable defects like crack and spalling appeared in various types of civil structures including beam; wall and column. These defects have a great influence on the usage life of the structures and hence need attention and maintenance. Normally, defect detection in civil structures is the human visual inspection. The manual approach is time-consuming and has a low accuracy. In this paper, we use deep learning method to predict multiple defects in various types of civil structures. Crack and spalling are recognized using the convolutional neural network model in a pixel level. To build the inspection model, the U-net Plus Plus architecture is used for segmentation. In the dataset, 1300 images are taken for training and 600 images for validation. The obtained computer vison model is tested on a dataset of about 491 images. The recall for crack and spalling reach 0.89 and 0.85 respectively. This research work develops an automated approach for defects detection in civil structures. The results can provide helpful guidance for structure health monitoring and maintenance in civil engineering like buildings and bridges.

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Section: Introductionmentioning

confidence: 99%

Defect Detection in Civil Structure Using Deep Learning Method

Zhou,

Tiong

2024

J. Phys.: Conf. Ser.

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Digital survey applied to the assessment of pathological manifestations in the architectural heritage of monte alegre in Piracicaba/SP

Rodrigues,

Favoreti,

Borges

et al. 2023

J Build Rehabil

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YOLO-Based UAV Technology: A Review of the Research and Its Applications

Chen

Zheng

et al. 2023

Drones

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In recent decades, scientific and technological developments have continued to increase in speed, with researchers focusing not only on the innovation of single technologies but also on the cross-fertilization of multidisciplinary technologies. Unmanned aerial vehicle (UAV) technology has seen great progress in many aspects, such as geometric structure, flight characteristics, and navigation control. The You Only Look Once (YOLO) algorithm was developed and has been refined over the years to provide satisfactory performance for the real-time detection and classification of multiple targets. In the context of technology cross-fusion becoming a new focus, researchers have proposed YOLO-based UAV technology (YBUT) by integrating the above two technologies. This proposed integration succeeds in strengthening the application of emerging technologies and expanding the idea of the development of YOLO algorithms and drone technology. Therefore, this paper presents the development history of YBUT with reviews of the practical applications of YBUT in engineering, transportation, agriculture, automation, and other fields. The aim is to help new users to quickly understand YBUT and to help researchers, consumers, and stakeholders to quickly understand the research progress of the technology. The future of YBUT is also discussed to help explore the application of this technology in new areas.

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Using digital image processing and Unmanned Aerial Vehicle (UAV) for identifying ceramic cladding detachment in building facades

Cited by 4 publications

References 24 publications

Defect Detection in Civil Structure Using Deep Learning Method

Defect Detection in Civil Structure Using Deep Learning Method

Digital survey applied to the assessment of pathological manifestations in the architectural heritage of monte alegre in Piracicaba/SP

YOLO-Based UAV Technology: A Review of the Research and Its Applications

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