Integrated pixel-level CNN-FCN crack detection via photogrammetric 3D texture mapping of concrete structures

Chaiyasarn, Krisada; Buatik, Apichat; Mohamad, Hisham; Zhou, Mingliang; Kongsilp, Sirisilp; Poovarodom, Nakhorn

doi:10.1016/j.autcon.2022.104388

Cited by 49 publications

(17 citation statements)

References 30 publications

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“…This can be explained by the fact that the A1 group was already well-balanced, with a variation of 501 images between the classes in a universe of 22,795. The surface of the smooth type would be the one that most resembled the surface of concrete; however, the results accuracies of A1 and B1 were 86.65% and 86.36%, respectively, which is below those of concrete studies like Chow et al [25], Ali et al [26], Islam et al [27], and Chaiysarn et al [28]. This may indicate that the application of computer vision in mortar coating images is actually more complex than in the case of concrete.…”

Section: Training Of Vgg16 From Transfer Of Learningmentioning

confidence: 68%

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Public Database of Cracks Images in Mortar Coating with Different Types of Surface Finishes

Garcia Sobrinho,

Piauhy Neto,

Fernandes

2023

Buildings

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The use of technology, such as artificial intelligence (AI), in production processes has been optimizing several industrial realities. In civil construction, AI can be used in different applications, one of which is building inspection. One of the difficulties in developing this type of study is the low number of public image databases that represent more general aspects of building wear. In view of this, the main objective of this research was to set up a public database of images of cracks in mortar coating, considering different types of surface finish—smooth type, scrapped type, and rough type. A database was created with 33,088 images that went through a systematic labeling process based on classes defined in the study. Network training was carried out through transfer learning using the VGG16 in different groupings of finishes. It was found that the training accuracy varies according to surface finish and data balancing. The finish of the scrapped type was the one that presented the lowest accuracy. The database presented several types of noise and was unbalanced in all categories defined in the labeling. In this way, it was possible to create a database that represented possible situations to be found in real inspections.

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Section: Training Of Vgg16 From Transfer Of Learningmentioning

confidence: 68%

“…The best research results were achieved using VGG16 and AlexNet, both with 99.9% accuracy. Chaiysarn et al [28] carried out integrated crack detection, from CNN-FCN, at pixel level via photogrammetric mapping of the 3D texture of concrete structures. The authors achieved 99.8% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Public Database of Cracks Images in Mortar Coating with Different Types of Surface Finishes

Garcia Sobrinho,

Piauhy Neto,

Fernandes

2023

Buildings

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“…This method can also contribute to the maintenance of existing buildings; for instance, ref. [7] proposed an advanced system for crack identification in large structures.…”

Section: Photomodelingmentioning

confidence: 99%

A Perspective on AI-Based Image Analysis and Utilization Technologies in Building Engineering: Recent Developments and New Directions

2023

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Artificial Intelligence (AI) is a trending topic in many research areas. In recent years, even building, civil, and structural engineering have also started to face with several new techniques and technologies belonging to this field, such as smart algorithms, big data analysis, deep learning practices, etc. This perspective paper collects the last developments on the use of AI in building engineering, highlighting what the authors consider the most stimulating scientific advancements of recent years, with a specific interest in the acquisition and processing of photographic surveys. Specifically, the authors want to focus both on the applications of artificial intelligence in the field of building engineering, as well as on the evolution of recently widespread technological equipment and tools, emphasizing their mutual integration. Therefore, seven macro-categories have been identified where these issues are addressed: photomodeling; thermal imaging; object recognition; inspections assisted by UAVs; FEM and BIM implementation; structural monitoring; and damage identification. For each category, the main new innovations and the leading research perspectives are highlighted. The article closes with a brief discussion of the primary results and a viewpoint for future lines of research.

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“…The size of the dataset directly affects the detection results in deep learning, and collecting various samples for training can achieve good generalization. Sample data are the key to improving detection results, and data are the driving force of high-performance frameworks [23]. This study preprocesses the images by rotating, mirroring, scaling and image enhancement to amplify the dataset.…”

Section: Image Preprocessingmentioning

confidence: 99%

Crack detection for concrete bridges with imaged based deep learning

et al. 2022

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Within the framework of intelligent bridge detection, a number of crack detection methods based on image processing techniques have been implemented. In this study, a combined novel approach with deep learning of a single shot multibox detector (SSD) and the eight neighborhood algorithm is proposed and applied to bridge crack image identification to provide an automatic method for crack detection. First, a large number of concrete crack images collected from the site were segmented and preprocessed for the establishment of a crack image dataset. Deep learning of the SSD algorithm was introduced on the training set to establish the detection model, where the model parameters were adjusted by the validation set. Sliding window technology was integrated to identify the cracks in the test set. The effects of the sliding window size and dataset size on the crack detection results were discussed. Moreover, the eight neighborhood algorithm was adopted for further crack detection correction. The results show that the configuration achieves good crack detection by the deep learning of the SSD algorithm with high precision and recall. The introduction of the eight neighborhood correction algorithm further improves the detection results by eliminating some misjudged results. Finally, the developed algorithm was placed into a portable device, with which cracks were effectively identified. The introduced method shows significantly better performance in crack detection, and the system installed on the portable device provides a way to broaden its application in the automatic crack detection of concrete bridges.

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Integrated pixel-level CNN-FCN crack detection via photogrammetric 3D texture mapping of concrete structures

Cited by 49 publications

References 30 publications

Public Database of Cracks Images in Mortar Coating with Different Types of Surface Finishes

Public Database of Cracks Images in Mortar Coating with Different Types of Surface Finishes

A Perspective on AI-Based Image Analysis and Utilization Technologies in Building Engineering: Recent Developments and New Directions

Crack detection for concrete bridges with imaged based deep learning

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